Lessons From Digital History’s Antecedents

The below is the transcript from my October 29 keynote presented to the Creativity and The City 1600-2000 conference in Amsterdam, titled “Punched-Card Humanities”. I survey historical approaches to quantitative history, how they relate to the nomothetic/idiographic divide, and discuss some lessons we can learn from past successes and failures. For ≈200 relevant references, see this Zotero folder.


Title Slide
Title Slide

I’m here to talk about Digital History, and what we can learn from its quantitative antecedents. If yesterday’s keynote was framing our mutual interest in the creative city, I hope mine will help frame our discussions around the bottom half of the poster; the eHumanities perspective.

Specifically, I’ve been delighted to see at this conference, we have a rich interplay between familiar historiographic and cultural approaches, and digital or eHumanities methods, all being brought to bear on the creative city. I want to take a moment to talk about where these two approaches meet.

Yesterday’s wonderful keynote brought up the complicated goal of using new digital methods to explore the creative city, without reducing the city to reductive indices. Are we living up to that goal? I hope a historical take on this question might help us move in this direction, that by learning from those historiographic moments when formal methods failed, we can do better this time.

Creativity Conference Theme
Creativity Conference Theme

Digital History is different, we’re told. “New”. Many of us know historians who used computers in the 1960s, for things like demography or cliometrics, but what we do today is a different beast.

Commenting on these early punched-card historians, in 1999, Ed Ayers wrote, quote, “the first computer revolution largely failed.” The failure, Ayers, claimed, was in part due to their statistical machinery not being up to the task of representing the nuances of human experience.

We see this rhetoric of newness or novelty crop up all the time. It cropped up a lot in pioneering digital history essays by Roy Rosenzweig and Dan Cohen in the 90s and 2000s, and we even see a touch of it, though tempered, in this conference’s theme.

In yesterday’s final discussion on uncertainty, Dorit Raines reminded us the difference between quantitative history in the 70s and today’s Digital History is that today’s approaches broaden our sources, whereas early approaches narrowed them.

Slide (r)evolution
Slide (r)evolution

To say “we’re at a unique historical moment” is something common to pretty much everyone, everywhere, forever. And it’s always a little bit true, right?

It’s true that every historical moment is unique. Unprecedented. Digital History, with its unique combination of public humanities, media-rich interests, sophisticated machinery, and quantitative approaches, is pretty novel.

But as the saying goes, history never repeats itself, but it rhymes. Each thread making up Digital History has a long past, and a lot of the arguments for or against it have been made many times before. Novelty is a convenient illusion that helps us get funding.

Not coincidentally, it’s this tension I’ll highlight today: between revolution and evolution, between breaks and continuities, and between the historians who care more about what makes a moment unique, and those who care more about what connects humanity together.

To be clear, I’m operating on two levels here: the narrative and the metanarrative. The narrative is that the history of digital history is one of continuities and fractures; the metanarrative is that this very tension between uniqueness and self-similarity is what swings the pendulum between quantitative and qualitative historians.

Now, my claim that debates over continuity and discontinuity are a primary driver of the quantitative/qualitative divide comes a bit out of left field — I know — so let me back up a few hundred years and explain.

Chronology
Chronology

Francis Bacon wrote that knowledge would be better understood if it were collected into orderly tables. His plea extended, of course, to historical knowledge, and inspired renewed interest in a genre already over a thousand years old: tabular chronology.

These chronologies were world histories, aligning the pasts of several regions which each reconned the passage of time differently.

Isaac Newton inherited this tradition, and dabbled throughout his life in establishing a more accurate universal chronology, aligning Biblical history with Greek legends and Egyptian pharoahs.

Newton brought to history the same mind he brought to everything else: one of stars and calculations. Like his peers, Newton relied on historical accounts of astronomical observations to align simultaneous events across thousands of miles. Kepler and Scaliger, among others, also partook in this “scientific history”.

Where Newton departed from his contemporaries, however, was in his use of statistics for sorting out history. In the late 1500s, the average or arithmetic mean was popularized by astronomers as a way of smoothing out noisy measurements. Newton co-opted this method to help him estimate the length of royal reigns, and thus the ages of various dynasties and kingdoms.

On average, Newton figured, a king’s reign lasted 18-20 years. If the history books record 5 kings, that means the dynasty lasted between 90 and 100 years.

Newton was among the first to apply averages to fill in chronologies, though not the first to apply them to human activities. By the late 1600s, demographic statistics of contemporary life — of births, burials and the like — were becoming common. They were ways of revealing divinely ordered regularities.

Incidentally, this is an early example of our illustrious tradition of uncritically appropriating methods from the natural sciences. See? We’ve all done it, even Newton!  

Joking aside, this is an important point: statistical averages represented divine regularities. Human statistics began as a means to uncover universal truths, and they continue to be employed in that manner. More on that later, though.

Musgrave Quote

Newton’s method didn’t quite pass muster, and skepticism grew rapidly on the whole prospect of mathematical history.

Criticizing Newton in 1782, for example, Samuel Musgrave argued, in part, that there are no discernible universal laws of history operating in parallel to the universal laws of nature. Nature can be mathematized; people cannot.

Not everyone agreed. Francesco Algarotti passionately argued that Newton’s calculation of average reigns, the application of math to history, was one of his greatest achievements. Even Voltaire tried Newton’s method, aligning a Chinese chronology with Western dates using average length of reigns.

Nomothetic / Idiographic
Nomothetic / Idiographic

Which brings us to the earlier continuity/discontinuity point: quantitative history stirs debate in part because it draws together two activities Immanuel Kant sets in opposition: the tendency to generalize, and the tendency to specify.

The tendency to generalize, later dubbed Nomothetic, often describes the sciences: extrapolating general laws from individual observations. Examples include the laws of gravity, the theory of evolution by natural selection, and so forth.

The tendency to specify, later dubbed Idiographic, describes, mostly, the humanities: understanding specific, contingent events in their own context and with awareness of subjective experiences. This could manifest as a microhistory of one parish in the French Revolution, a critical reading of Frankenstein focused on gender dynamics, and so forth.  

These two approaches aren’t mutually exclusive, and they frequently come in contact around scholarship of the past. Paleontologists, for example, apply general laws of biology and geology to tell the specific story of prehistoric life on Earth. Astronomers, similarly, combine natural laws and specific observations to trace to origins of our universe.

Historians have, with cyclically recurring intensity, engaged in similar efforts. One recent nomothetic example is that of cliodynamics: the practitioners use data and simulations to discern generalities such as why nations fail or what causes war. Recent idiographic historians associate more with the cultural and theoretical turns in historiography, often focusing on microhistories or the subjective experiences of historical actors.

Both tend to meet around quantitative history, but the conversation began well before the urge to quantify. They often fruitfully align and improve one another when working in concert; for example when the historian cites a common historical pattern in order to highlight and contextualize an event which deviates from it.

But more often, nomothetic and idiographic historians find themselves at odds. Newton extrapolated “laws” for the length of kings, and was criticized for thinking mathematics had any place in the domain of the uniquely human. Newton’s contemporaries used human statistics to argue for divine regularities, and this was eventually criticized as encroaching on human agency, free will, and the uniqueness of subjective experience.

Bacon Taxonomy
Bacon Taxonomy

I’ll highlight some moments in this debate, focusing on English-speaking historians, and will conclude with what we today might learn from foibles of the quantitative historians who came before.

Let me reiterate, though, that quantitative is not nomothetic history, but they invite each other, so I shouldn’t be ahistorical by dividing them.

Take Henry Buckle, who in 1857 tried to bridge the two-culture divide posed by C.P. Snow a century later. He wanted to use statistics to find general laws of human progress, and apply those generalizations to the histories of specific nations.

Buckle was well-aware of historiography’s place between nomothetic and idiographic cultures, writing: “it is the business of the historian to mediate between these two parties, and reconcile their hostile pretensions by showing the point at which their respective studies ought to coalesce.”

In direct response, James Froud wrote that there can be no science of history. The whole idea of Science and History being related was nonsensical, like talking about the colour of sound. They simply do not connect.

This was a small exchange in a much larger Victorian debate pitting narrative history against a growing interest in scientific history. The latter rose on the coattails of growing popular interest in science, much like our debates today align with broader discussions around data science, computation, and the visible economic successes of startup culture.

This is, by the way, contemporaneous with something yesterday’s keynote highlighted: the 19th century drive to establish ‘urban laws’.

By now, we begin seeing historians leveraging public trust in scientific methods as a means for political control and pushing agendas. This happens in concert with the rise of punched cards and, eventually, computational history. Perhaps the best example of this historical moment comes from the American Census in the late 19th century.

19C Map
19C Map

Briefly, a group of 19th century American historians, journalists, and census chiefs used statistics, historical atlases, and the machinery of the census bureau to publicly argue for the disintegration of the U.S. Western Frontier in the late 19th century.

These moves were, in part, made to consolidate power in the American West and wrestle control from the native populations who still lived there. They accomplished this, in part, by publishing popular atlases showing that the western frontier was so fractured that it was difficult to maintain and defend. 1

The argument, it turns out, was pretty compelling.

Hollerith Cards
Hollerith Cards

Part of what drove the statistical power and scientific legitimacy of these arguments was the new method, in 1890, of entering census data on punched cards and processing them in tabulating machines. The mechanism itself was wildly successful, and the inventor’s company wound up merging with a few others to become IBM. As was true of punched-card humanities projects through the time of Father Roberto Busa, this work was largely driven by women.

It’s worth pausing to remember that the history of punch card computing is also a history of the consolidation of government power. Seeing like a computer was, for decades, seeing like a state. And how we see influences what we see, what we care about, how we think.  

Recall the Ed Ayers quote I mentioned at the beginning of his talk. He said the statistical machinery of early quantitative historians could not represent the nuance of historical experience. That doesn’t just mean the math they used; it means the actual machinery involved.

See, one of the truly groundbreaking punch card technologies at the turn of the century was the card sorter. Each card could represent a person, or household, or whatever else, which is sort of legible one-at-a-time, but unmanageable in giant stacks.

Now, this is still well before “computers”, but machines were being developed which could sort these cards into one of twelve pockets based on which holes were punched. So, for example, if you had cards punched for people’s age, you could sort the stacks into 10 different pockets to break them up by age groups: 0-9, 10-19, 20-29, and so forth.

This turned out to be amazing for eyeball estimates. If your 20-29 pocket was twice as full as your 10-19 pocket after all the cards were sorted, you had a pretty good idea of the age distribution.

Over the next 50 years, this convenience would shape the social sciences. Consider demographics or marketing. Both developed in the shadow of punch cards, and both relied heavily on what’s called “segmentation”, the breaking of society into discrete categories based on easily punched attributes. Age ranges, racial background, etc. These would be used to, among other things, determine who was interested in what products.

They’d eventually use statistics on these segments to inform marketing strategies.

But, if you look at the statistical tests that already existed at the time, these segmentations weren’t always the best way to break up the data. For example, age flows smoothly between 0 and 100; you could easily contrive a statistical test to show that, as a person ages, she’s more likely to buy one product over another, over a set of smooth functions.

That’s not how it worked though. Age was, and often still is, chunked up into ten or so distinct ranges, and those segments were each analyzed individually, as though they were as distinct from one another as dogs and cats. That is, 0-9 is as related to 10-19 as it is to 80-89.

What we see here is the deep influence of technological affordances on scholarly practice, and it’s an issue we still face today, though in different form.

As historians began using punch cards and social statistics, they inherited, or appropriated, a structure developed for bureaucratic government processing, and were rightly soon criticized for its dehumanizing qualities.

Pearson Stats

Unsurprisingly, given this backdrop, historians in the first few decades of the 20th century often shied away from or rejected quantification.

The next wave of quantitative historians, who reached their height in the 1930s, approached the problem with more subtlety than the previous generations in the 1890s and 1860s.

Charles Beard’s famous Economic Interpretation of the Constitution of the United States used economic and demographic stats to argue that the US Constitution was economically motivated. Beard, however, did grasp the fundamental idiographic critique of quantitative history, claiming that history was, quote:

“beyond the reach of mathematics — which cannot assign meaningful values to the imponderables, immeasurables, and contingencies of history.”

The other frequent critique of quantitative history, still heard, is that it uncritically appropriates methods from stats and the sciences.

This also wasn’t entirely true. The slide behind me shows famed statistician Karl Pearson’s attempt to replicate the math of Isaac Newton that we saw earlier using more sophisticated techniques.

By the 1940s, Americans with graduate training in statistics like Ernest Rubin were actively engaging historians in their own journals, discussing how to carefully apply statistics to historical research.

On the other side of the channel, the French Annales historians were advocating longue durée history; a move away from biographies to prosopographies, from events to structures. In its own way, this was another historiography teetering on the edge between the nomothetic and idiographic, an approach that sought to uncover the rhymes of history.

Interest in quantitative approaches surged again in the late 1950s, led by a new wave of Annales historians like Fernand Braudel and American quantitative manifestos like those by Benson, Conrad, and Meyer.

William Aydolette went so far as to point out that all historians implicitly quantify, when they use words like “many”, “average”, “representative”, or “growing” – and the question wasn’t can there be quantitative history, but when should formal quantitative methods be utilized?

By 1968, George Murphy, seeing the swell of interest, asked a very familiar question: why now? He asked why the 1960s were different from the 1860s or 1930s, why were they, in that historical moment, able to finally do it right? His answer was that it wasn’t just the new technologies, the huge datasets, the innovative methods: it was the zeitgeist. The 1960s was the right era for computational history, because it was the era of computation.

By the early 70s, there was a historian using a computer in every major history department. Quantitative history had finally grown into itself.

Popper Historicism
Popper Historicism

Of course, in retrospect, Murphy was wrong. Once the pendulum swung too far towards scientific history, theoretical objections began pushing it the other way.

In Poverty of Historicism, Popper rejected scientific history, but mostly as a means to reject historicism outright. Popper’s arguments represent an attack from outside the historiographic tradition, but one that eventually had significant purchase even among historians, as an indication of the failure of nomothetic approaches to culture. It is, to an extent, a return to Musgrave’s critique of Isaac Newton.

At the same time, we see growing criticism from historians themselves. Arthur Schlesinger famously wrote that “important questions are important precisely because they are not susceptible to quantitative answers.”

There was a converging consensus among English-speaking historians, as in the early 20th century, that quantification erased the essence of the humanities, that it smoothed over the very inequalities and historical contingencies we needed to highlight.

Barzun's Clio
Barzun’s Clio

Jacques Barzun summed it up well, if scathingly, saying history ought to free us from the bonds of the machine, not feed us into it.

The skeptics prevailed, and the pendulum swung the other way. The post-structural, cultural, and literary-critical turns in historiography pivoted away from quantification and computation. The final nail was probably Fogel and Engerman’s 1974 Time on the Cross, which reduced the Atlantic  slave-trade to economic figures, and didn’t exactly treat the subject with nuance and care.

The cliometricians, demographers, and quantitative historians didn’t disappear after the cultural turn, but their numbers shrunk, and they tended to find themselves in social science departments, or fled here to Europe, where social and economic historians were faring better.

Which brings us, 40 years on, to the middle of a new wave of quantitative or “formal method” history. Ed Ayers, like George Murphy before him, wrote, essentially, this time it’s different.

And he’s right, to a point. Many here today draw their roots not to the cliometricians, but to the very cultural historians who rejected quantification in the first place. Ours is a digital history steeped in the the values of the cultural turn, that respects social justice and seeks to use our approaches to shine a light on the underrepresented and the historically contingent.

But that doesn’t stop a new wave of critiques that, if not repeating old arguments, certainly rhymes. Take Johanna Drucker’s recent call to rebrand data as capta, because when we treat observations objectively as if it were the same as the phenomena observed, we collapse the critical distance between the world and our interpretation of it. And interpretation, Drucker contends, is the foundation on which humanistic knowledge is based.

Which is all to say, every swing of the pendulum between idiographic and nomothetic history was situated in its own historical moment. It’s not a clock’s pendulum, but Foucault’s pendulum, with each swing’s apex ending up slightly off from the last. The issues of chronology and astronomy are different from those of eugenics and manifest destiny, which are themselves different from the capitalist and dehumanizing tendencies of 1950s mainframes.

But they all rhyme. Quantitative history has failed many times, for many reasons, but there are a few threads that bind them which we can learn from — or, at least, a few recurring mistakes we can recognize in ourselves and try to avoid going forward.

We won’t, I suspect, stop the pendulum’s inevitable about-face, but at least we can continue our work with caution, respect, and care.

Which is to be Master?
Which is to be Master?

The lesson I’d like to highlight may be summed up in one question, asked by Humpty Dumpty to Alice: which is to be master?

Over several hundred years of quantitative history, the advice of proponents and critics alike tends to align with this question. Indeed in 1956, R.G. Collingwood wrote specifically “statistical research is for the historian a good servant but a bad master,” referring to the fact that statistical historical patterns mean nothing without historical context.

Schlesinger, the guy who I mentioned earlier who said historical questions are interesting precisely because they can’t be quantified, later acknowledged that while quantitative methods can be useful, they’ll lead historians astray. Instead of tackling good questions, he said, historians will tackle easily quantifiable ones — and Schlesinger was uncomfortable by the tail wagging the dog.

Which is to be master - questions
Which is to be master – questions

I’ve found many ways in which historians have accidentally given over agency to their methods and machines over the years, but these five, I think, are the most relevant to our current moment.

Unfortunately since we running out of time, you’ll just have to trust me that these are historically recurring.

Number 1 is the uncareful appropriation of statistical methods for historical uses. It controls us precisely because it offers us a black box whose output we don’t truly understand.

A common example I see these days is in network visualizations. People visualize nodes and edges using what are called force-directed layouts in Gephi, but they don’t exactly understand what those layouts mean. As these layouts were designed, physical proximity of nodes are not meant to represent relatedness, yet I’ve seen historians interpret two neighboring nodes as being related because of their visual adjacency.

This is bad. It’s false. But because we don’t quite understand what’s happening, we get lured by the black box into nonsensical interpretations.

The second way methods drive us is in our reliance on methodological imports. That is, we take the time to open the black box, but we only use methods that we learn from statisticians or scientists. Even when we fully understand the methods we import, if we’re bound to other people’s analytic machinery, we’re bound to their questions and biases.

Take the example I mentioned earlier, with demographic segmentation, punch card sorters, and its influence on social scientific statistics. The very mechanical affordances of early computers influence the sort of questions people asked for decades: how do discrete groups of people react to the world in different ways, and how do they compare with one another?

The next thing to watch out for is naive scientism. Even if you know the assumptions of your methods, and you develop your own techniques for the problem at hand, you still can fall into the positivist trap that Johanna Drucker warns us about — collapsing the distance between what we observe and some underlying “truth”.

This is especially difficult when we’re dealing with “big data”. Once you’re working with so much material you couldn’t hope to read it all, it’s easy to be lured into forgetting the distance between operationalizations and what you actually intend to measure.

For instance, if I’m finding friendships in Early Modern Europe by looking for particular words being written in correspondences, I will completely miss the existence of friends who were neighbors, and thus had no reason to write letters for us to eventually read.

A fourth way we can be mislead by quantitative methods is the ease with which they lend an air of false precision or false certainty.

This is the problem Matthew Lincoln and the other panelists brought up yesterday, where missing or uncertain data, once quantified, falsely appears precise enough to make comparisons.

I see this mistake crop up in early and recent quantitative histories alike; we measure, say, the changing rate of transnational shipments over time, and notice a positive trend. The problem is the positive difference is quite small, easily attributable to error, but because numbers are always precise, it still feels like we’re being more precise than doing a qualitative assessment. Even when it’s unwarranted.

The last thing to watch out for, and maybe the most worrisome, is the blinders quantitative analysis places on historians who don’t engage in other historiographic methods. This has been the downfall of many waves of quantitative history in the past; the inability to care about or even see that which can’t be counted.

This was, in part, was what led Time on the Cross to become the excuse to drive historians from cliometrics. The indicators of slavery that were measurable were sufficient to show it to have some semblance of economic success for black populations; but it was precisely those aspects of slavery they could not measure that were the most historically important.

So how do we regain mastery in light of these obstacles?

Which is to be master - answers
Which is to be master – answers

1. Uncareful Appropriation – Collaboration

Regarding the uncareful appropriation of methods, we can easily sidestep the issue of accidentally misusing a method by collaborating with someone who knows how the method works. This may require a translator; statisticians can as easily misunderstand historical problems as historians can misunderstand statistics.

Historians and statisticians can fruitfully collaborate, though, if they have someone in the middle trained to some extent in both — even if they’re not themselves experts. For what it’s worth, Dutch institutions seem to be ahead of the game in this respect, which is something that should be fostered.

2. Reliance on Imports – Statistical Training

Getting away from reliance on disciplinary imports may take some more work, because we ourselves must learn the approaches well enough to augment them, or create our own. Right now in DH this is often handled by summer institutes and workshop series, but I’d argue those are not sufficient here. We need to make room in our curricula for actual methods courses, or even degrees focused on methodology, in the same fashion as social scientists, if we want to start a robust practice of developing appropriate tools for our own research.

3. Naive Scientism – Humanities History

The spectre of naive scientism, I think, is one we need to be careful of, but we are also already well-equipped to deal with it. If we want to combat the uncareful use of proxies in digital history, we need only to teach the history of the humanities; why the cultural turn happened, what’s gone wrong with positivistic approaches to history in the past, etc.

Incidentally, I think this is something digital historians already guard well against, but it’s still worth keeping in mind and making sure we teach it. Particularly, digital historians need to remain aware of parallel approaches from the past, rather than tracing their background only to the textual work of people like Roberto Busa in Italy.

4. False Precision & Certainty – Simulation & Triangulation

False precision and false certainty have some shallow fixes, and some deep ones. In the short term, we need to be better about understanding things like confidence intervals and error bars, and use methods like what Matthew Lincoln highlighted yesterday.

In the long term, though, digital history would do well to adopt triangulation strategies to help mitigate against these issues. That means trying to reach the same conclusion using multiple different methods in parallel, and seeing if they all agree. If they do, you can be more certain your results are something you can trust, and not just an accident of the method you happened to use.

5. Quantitative Blinders – Rejecting Digital History

Avoiding quantitative blinders – that is, the tendency to only care about what’s easily countable – is an easy fix, but I’m afraid to say it, because it might put me out of a job. We can’t call what we do digital history, or quantitative history, or cliometrics, or whatever else. We are, simply, historians.

Some of us use more quantitative methods, and some don’t, but if we’re not ultimately contributing to the same body of work, both sides will do themselves a disservice by not bringing every approach to bear in the wide range of interests historians ought to pursue.

Qualitative and idiographic historians will be stuck unable to deal with the deluge of material that can paint us a broader picture of history, and quantitative or nomothetic historians will lose sight of the very human irregularities that make history worth studying in the first place. We must work together.

If we don’t come together, we’re destined to remain punched-card humanists – that is, we will always be constrained and led by our methods, not by history.

Creativity Theme Again
Creativity Theme Again

Of course, this divide is a false one. There are no purely quantitative or purely qualitative studies; close-reading historians will continue to say things like “representative” or “increasing”, and digital historians won’t start publishing graphs with no interpretation.

Still, silos exist, and some of us have trouble leaving the comfort of our digital humanities conferences or our “traditional” history conferences.

That’s why this conference, I think, is so refreshing. It offers a great mix of both worlds, and I’m privileged and thankful to have been able to attend. While there are a lot of lessons we can still learn from those before us, from my vantage point, I think we’re on the right track, and I look forward to seeing more of those fruitful combinations over the course of today.

Thank you.

Notes:

  1. This account is influenced from some talks by Ben Schmidt. Any mistakes are from my own faulty memory, and not from his careful arguments.

[f-s d] Cetus

Quoting Liz Losh, Jacqueline Wernimont tweeted that behind every visualization is a spreadsheet.

But what, I wondered, is behind every spreadsheet?

Space whales.

Okay, maybe space whales aren’t behind every spreadsheet, but they’re behind this one, dated 1662, notable for the gigantic nail it hammered into the coffin of our belief that heaven above is perfect and unchanging. The following post is the first in my new series full-stack dev (f-s d), where I explore the secret life of data. 1

Hevelius. Mercurius in Sole visus (1662).
Hevelius. Mercurius in Sole visus (1662).

The Princess Bride teaches us a good story involves “fencing, fighting, torture, revenge, giants, monsters, chases, escapes, true love, miracles”. In this story, Cetus, three of those play a prominent role: (red) giants, (sea) monsters, and (cosmic) miracles. Also Greek myths, interstellar explosions, beer-brewing astronomers, meticulous archivists, and top-secret digitization facilities. All together, they reveal how technologies, people, and stars aligned to stick this 350-year-old spreadsheet in your browser today.

The Sea

When Aethiopian queen Cassiopeia claimed herself more beautiful than all the sea nymphs, Poseidon was, let’s say, less than pleased. Mildly miffed. He maybe sent a sea monster named Cetus to destroy Aethiopia.

Because obviously the best way to stop a flood is to drown a princess, Queen Cassiopeia chained her daughter to the rocks as a sacrifice to Cetus. Thankfully the hero Perseus just happened to be passing through Aethiopia, returning home after beheading Medusa, that snake-haired woman whose eyes turned living creatures to stone. Perseus (depicted below as the world’s most boring 2-ball juggler) revealed Medusa’s severed head to Cetus, turning the sea monster to stone and saving the princess. And then they got married because traditional gender roles I guess?

Corinthian vase depicting Perseus, Andromeda and Ketos.
Corinthian vase depicting Perseus, Andromeda and Ketos. [via]
Cetaceans, you may recall from grade school, are those giant carnivorous sea-mammals that Captain Ahab warned you about. Cetaceans, from Cetus. You may also remember we have a thing for naming star constellations and dividing the sky up into sections (see the Zodiac), and that we have a long history of comparing the sky to the ocean (see Carl Sagan or Star Trek IV).

It should come as no surprise, then, that we’ve designated a whole section of space as ‘The Sea‘, home of Cetus (the whale), Aquarius (the God) and Eridanus (the water pouring from Aquarius’ vase, source of river floods), Pisces (two fish tied together by a rope, which makes total sense I promise), Delphinus (the dolphin), and Capricornus (the goat-fish. Listen, I didn’t make these up, okay?).

Jamieson's Celestial Atlas, Plate 21 (1822).
Jamieson’s Celestial Atlas, Plate 21 (1822). [via]
Jamieson's Celestial Atlas, Plate 23 (1822).
Jamieson’s Celestial Atlas, Plate 23 (1822). [via]
Ptolemy listed most of these constellations in his Almagest (ca. 150 A.D.), including Cetus, along with descriptions of over a thousand stars. Ptolemy’s model, with Earth at the center and the constellations just past Saturn, set the course of cosmology for over a thousand years.

Ptolemy's Cosmos [by Robert A. Hatch]
Ptolemy’s Cosmos [by Robert A. Hatch]
In this cosmos, reigning in Western Europe for centuries past Copernicus’ death in 1543, the stars were fixed and motionless. There was no vacuum of space; every planet was embedded in a shell made of aether or quintessence (quint-essence, the fifth element), and each shell sat atop the next until reaching the celestial sphere. This last sphere held the stars, each one fixed to it as with a pushpin. Of course, all of it revolved around the earth.

The domain of heavenly spheres was assumed perfect in all sorts of ways. They slid across each other without friction, and the planets and stars were perfect spheres which could not change and were unmarred by inconsistencies. One reason it was so difficult for even “great thinkers” to believe the earth orbited the sun, rather than vice-versa, was because such a system would be at complete odds with how people knew physics to work. It would break gravity, break motion, and break the outer perfection of the cosmos, which was essential (…heh) 2 to our notions of, well, everything.

Which is why, when astronomers with their telescopes and their spreadsheets started systematically observing imperfections in planets and stars, lots of people didn’t believe them—even other astronomers. Over the course of centuries, though, these imperfections became impossible to ignore, and helped launch the earth in rotation ’round the sun.

This is the story of one such imperfection.

A Star is Born (and then dies)

Around 1296 A.D., over the course of half a year, a red dwarf star some 2 quadrillion miles away grew from 300 to 400 times the size of our sun. Over the next half year, the star shrunk back down to its previous size. Light from the star took 300 years to reach earth, eventually striking the retina of German pastor David Fabricius. It was very early Tuesday morning on August 13, 1596, and Pastor Fabricius was looking for Jupiter. 3

At that time of year, Jupiter would have been near the constellation Cetus (remember our sea monster?), but Fabricius noticed a nearby bright star (labeled ‘Mira’ in the below figure) which he did not remember from Ptolemy or Tycho Brahe’s star charts.

Mira Ceti and Jupiter. [via]
Mira Ceti and Jupiter. [via]
Spotting an unrecognized star wasn’t unusual, but one so bright in so common a constellation was certainly worthy of note. He wrote down some observations of the star throughout September and October, after which it seemed to have disappeared as suddenly as it appeared. The disappearance prompted Fabricius to write a letter about it to famed astronomer Tycho Brahe, who had described a similar appearing-then-disappearing star between 1572 and 1574. Brahe jotted Fabricius’ observations down in his journal. This sort of behavior, after all, was a bit shocking for a supposedly fixed and unchanging celestial sphere.

More shocking, however, was what happened 13 years later, on February 15, 1609. Once again searching for Jupiter, pastor Fabricius spotted another new star in the same spot as the last one. Tycho Brahe having recently died, Fabricius wrote a letter to his astronomical successor, Johannes Kepler, describing the miracle. This was unprecedented. No star had ever vanished and returned, and nobody knew what to make of it.

Unfortunately for Fabricius, nobody did make anything of it. His observations were either ignored or, occasionally, dismissed as an error. To add injury to insult, a local goose thief killed Fabricius with a shovel blow, thus ending his place in this star’s story, among other stories.

Mira Ceti

Three decades passed. On the winter solstice, 1638, Johannes Phocylides Holwarda prepared to view a lunar eclipse. He reported with excitement the star’s appearance and, by August 1639, its disappearance. The new star, Holwarda claimed, should be considered of the same class as Brahe, Kepler, and Fabricius’ new stars. As much a surprise to him as Fabricius, Holwarda saw the star again on November 7, 1639. Although he was not aware of it, his new star was the same as the one Fabricius spotted 30 years prior.

Two more decades passed before the new star in the neck of Cetus would be systematically sought and observed, this time by Johannes Hevelius: local politician, astronomer, and brewer of fine beers. By that time many had seen the star, but it was difficult to know whether it was the same celestial body, or even what was going on.

Hevelius brought everything together. He found recorded observations from Holwarda, Fabricius, and others, from today’s Netherlands to Germany to Poland, and realized these disparate observations were of the same star. Befitting its puzzling and seemingly miraculous nature, Hevelius dubbed the star Mira (miraculous) Ceti. The image below, from Hevelius’ Firmamentum Sobiescianum sive Uranographia (1687), depicts Mira Ceti as the bright star in the sea monster’s neck.

Hevelius. Firmamentum Sobiescianum sive Uranographia (1687).
Hevelius. Firmamentum Sobiescianum sive Uranographia (1687).

Going further, from 1659 to 1683, Hevelius observed Mira Ceti in a more consistent fashion than any before. There were eleven recorded observations in the 65 years between Fabricius’ first sighting of the star and Hevelius’ undertaking; in the following three, he had recorded 75 more such observations. Oddly, while Hevelius was a remarkably meticulous observer, he insisted the star was inherently unpredictable, with no regularity in its reappearances or variable brightness.

Beginning shortly after Hevelius, the astronomer Ismaël Boulliau also undertook a thirty year search for Mira Ceti. He even published a prediction, that the star would go through its vanishing cycle every 332 days, which turned out to be incredibly accurate. As today’s astronomers note, Mira Ceti‘s brightness increases and decreases by several orders of magnitude every 331 days, caused by an interplay between radiation pressure and gravity in the star’s gaseous exterior.

Mira Ceti composite taken by NASA's Galaxy Evolution Explorer. [via]
Mira Ceti composite taken by NASA’s Galaxy Evolution Explorer. [via]
While of course Boulliau didn’t arrive at today’s explanation for Mira‘s variability, his solution did require a rethinking of the fixity of stars, and eventually contributed to the notion that maybe the same physical laws that apply on Earth also rule the sun and stars.

Spreadsheet Errors

But we’re not here to talk about Boulliau, or Mira Ceti. We’re here to talk about this spreadsheet:

Hevelius. Mercurius in Sole visus (1662).
Hevelius. Mercurius in Sole visus (1662).

This snippet represents Hevelius’ attempt to systematically collected prior observations of Mira Ceti. Unreasonably meticulous readers of this post may note an inconsistency: I wrote that Johannes Phocylides Holwarda observed Mira Ceti on November 7th, 1639, yet Hevelius here shows Holwarda observing the star on December 7th, 1639, an entire month later. The little notes on the side are basically the observers saying: “wtf this star keeps reappearing???”

This mistake was not a simple printer’s error. It reappeared in Hevelius’ printed books three times: 1662, 1668, and 1685. This is an early example of what Raymond Panko and others call a spreadsheet error, which appear in nearly 90% of 21st century spreadsheets. Hand-entry is difficult, and mistakes are bound to happen. In this case, a game of telephone also played a part: Hevelius may have pulled some observations not directly from the original astronomers, but from the notes of Tycho Brahe and Johannes Kepler, to which he had access.

Unfortunately, with so few observations, and many of the early ones so sloppy, mistakes compound themselves. It’s difficult to predict a variable star’s periodicity when you don’t have the right dates of observation, which may have contributed to Hevelius’ continued insistence that Mira Ceti kept no regular schedule. The other contributing factor, of course, is that Hevelius worked without a telescope and under cloudy skies, and stars are hard to measure under even the best circumstances.

To Be Continued

Here ends the first half of Cetus. The second half will cover how Hevelius’ book was preserved, the labor behind its digitization, and a bit about the technologies involved in creating the image you see.

Early modern astronomy is a particularly good pre-digital subject for full-stack dev (f-s d), since it required vast international correspondence networks and distributed labor in order to succeed. Hevelius could not have created this table, compiled from the observations of several others, without access to cutting-edge astronomical instruments and the contemporary scholarly network.

You may ask why I included that whole section on Greek myths and Ptolemy’s constellations. Would as many early modern astronomers have noticed Mira Ceti had it not sat in the center of a familiar constellation, I wonder?

I promised this series will be about the secret life of data, answering the question of what’s behind a spreadsheet. Cetus is only the first story (well, second, I guess), but the idea is to upturn the iceberg underlying seemingly mundane datasets to reveal the complicated stories of their creation and usage. Stay-tuned for future installments.

Notes:

  1. I’m retroactively adding my blog rant about data underlying an equality visualization to the f-s d series.
  2. this pun is only for historians of science
  3. Most of the historiography in this and the following section are summarized from Robert A. Hatch’s “Discovering Mira Ceti: Celestial Change and Cosmic Continuity

“Digital History” Can Never Be New

If you claim computational approaches to history (“digital history”) lets historians ask new types of questions, or that they offer new historical approaches to answering or exploring old questions, you are wrong. You’re not actually wrong, but you are institutionally wrong, which is maybe worse.

This is a problem, because rhetoric from practitioners (including me) is that we can bring some “new” to the table, and when we don’t, we’re called out for not doing so. The exchange might (but probably won’t) go like this:

Digital Historian: And this graph explains how velociraptors were of utmost importance to Victorian sensibilities.

Historian in Audience: But how is this telling us anything we haven’t already heard before? Didn’t John Hammond already make the same claim?

DH: That’s true, he did. One thing the graph shows, though, is that velicoraptors in general tend to play much more unimportant roles across hundreds of years, which lends support to the Victorian thesis.

HiA: Yes, but the generalized argument doesn’t account for cultural differences across those times, so doesn’t meaningfully contribute to this (or any other) historical conversation.


New Questions

History (like any discipline) is made of people, and those people have Ideas about what does or doesn’t count as history (well, historiography, but that’s a long word so let’s ignore it). If you ask a new type of question or use a new approach, that new thing probably doesn’t fit historians’ Ideas about proper history.

Take culturomics. They make claims like this:

The age of peak celebrity has been consistent over time: about 75 years after birth. But the other parameters have been changing. Fame comes sooner and rises faster. Between the early 19th century and the mid-20th century, the age of initial celebrity declined from 43 to 29 years, and the doubling time fell from 8.1 to 3.3 years.

Historians saw those claims and asked “so what”? It’s not interesting or relevant according to the things historians usually consider interesting or relevant, and it’s problematic in ways historians find things problematic. For example, it ignores cultural differences, does not speak to actual human experiences, and has nothing of use to say about a particular historical moment.

It’s true. Culturomics-style questions do not fit well within a humanities paradigm (incommensurable, anyone?). By the standard measuring stick of what makes a good history project, culturomics does not measure up. A new type of question requires a new measuring stick; in this case, I think a good one for culturomics-style approaches is the extent to which they bridge individual experiences with large-scale social phenomena, or how well they are able to reconcile statistical social regularities with free or contingent choice.

The point, though, is a culturomics presentation would fit few of the boxes expected at a history conference, and so would be considered a failure. Rightly so, too—it’s a bad history presentation. But what culturomics is successfully doing is asking new types of questions, whether or not historians find them legitimate or interesting. Is it good culturomics?

To put too fine a point on it, since history is often a question-driven discipline, new types of questions that are too different from previous types are no longer legitimately within the discipline of history, even if they are intrinsically about human history and do not fit in any other discipline.

What’s more, new types of questions may appear simplistic by historian’s standards, because they fail at fulfilling even the most basic criteria usually measuring historical worth. It’s worth keeping in mind that, to most of the rest of the world, our historical work often fails at meeting their criteria for worth.

New Approaches

New approaches to old questions share a similar fate, but for different reasons. That is, if they are novel, they are not interesting, and if they are interesting, they are not novel.

Traditional historical questions are, let’s face it, not particularly new. Tautologically. Some old questions in my field are: what role did now-silent voices play in constructing knowledge-making instruments in 17th century astronomy? How did scholarship become institutionalized in the 18th century? Why was Isaac Newton so annoying?

My own research is an attempt to provide a broader view of those topics (at least, the first two) using computational means. Since my topical interest has a rich tradition among historians, it’s unlikely any of my historically-focused claims (for example, that scholarly institutions were built to replace the really complicated and precarious role people played in coordinating social networks) will be without precedent.

After decades, or even centuries, of historical work in this area, there will always be examples of historians already having made my claims. My contribution is the bolstering of a particular viewpoint, the expansion of its applicability, the reframing of a discussion. Ultimately, maybe, I convince the world that certain social network conditions play an important role in allowing scholarly activity to be much more successful at its intended goals. My contribution is not, however, a claim that is wholly without precedent.

But this is a problem, since DH rhetoric, even by practitioners, can understandably lead people to expect such novelty. Historians in particular are very good at fitting old patterns to new evidence. It’s what we’re trained to do.

Any historical claim (to an acceptable question within the historical paradigm) can easily be countered with “but we already knew that”. Either the question’s been around long enough that every plausible claim has been covered, or the new evidence or theory is similar enough to something pre-existing that it can be taken as precedent.

The most masterful recent discussion of this topic was Matthew Lincoln’s Confabulation in the humanities, where he shows how easy it is to make up evidence and get historians to agree that they already knew it was true.

To put too fine a point on it, new approaches to old historical questions are destined to produce results which conform to old approaches; or if they don’t, it’s easy enough to stretch the old & new theories together until they fit. New approaches to old questions will fail at producing completely surprising results; this is a bad standard for historical projects. If a novel methodology were to create truly unrecognizable results, it is unlikely those results would be recognized as “good history” within the current paradigm. That is, historians would struggle to care.

What Is This Beast?

What is this beast we call digital history? Boundary-drawing is a tried-and-true tradition in the humanities, digital or otherwise. It’s theoretically kind of stupid but practically incredibly important, since funding decisions, tenure cases, and similar career-altering forces are at play. If digital history is a type of history, it’s fundable as such, tenurable as such; if it isn’t, it ain’t. What’s more, if what culturomics researchers are doing are also history, their already-well-funded machine can start taking slices of the sad NEH pie.

Artist's rendition of sad NEH pie. [via]
Artist’s rendition of sad NEH pie. [via]
So “what counts?” is unfortunately important to answer.

This discussion around what is “legitimate history research” is really important, but I’d like to table it for now, because it’s so often conflated with the discussion of what is “legitimate research” sans history. The former question easily overshadows the latter, since academics are mostly just schlubs trying to make a living.

For the last century or so, history and philosophy of science have been smooshed together in departments and conferences. It’s caused a lot of concern. Does history of science need philosophy of science? Does philosophy of science need history of science? What does it mean to combine the two? Is what comes out of the middle even useful?

Weirdly, the question sometimes comes down to “does history and philosophy of science even exist?”. It’s weird because people identify with that combined title, so I published a citation analysis in Erkenntnis a few years back that basically showed that, indeed, there is an area between the two communities, and indeed those people describe themselves as doing HPS, whatever that means to them.

Look! Right in the middle there, it's history and philosophy of science.
Look! Right in the middle there, it’s history and philosophy of science.

I bring this up because digital history, as many of us practice it, leaves us floating somewhere between public engagement, social science, and history. Culturomics occupies a similar interstitial space, though inching closer to social physics and complex systems.

From this vantage point, we have a couple of options. We can say digital history is just history from a slightly different angle, and try to be evaluated by standard historical measuring sticks—which would make our work easily criticized as not particularly novel. Or we can say digital history is something new, occupying that in-between space—which could render the work unrecognizable to our usual communities.

The either/or proposition is, of course, ludicrous. The best work being done now skirts the line, offering something just novel enough to be surprising, but not so out of traditional historical bounds as to be grouped with culturomics. But I think we need to more deliberate and organized in this practice, lest we want to be like History and Philosophy of Science, still dealing with basic questions of legitimacy fifty years down the line.

In the short term, this probably means trying not just to avoid the rhetoric of newness, but to actively curtail it. In the long term, it may mean allying with like-minded historians, social scientists, statistical physicists, and complexity scientists to build a new framework of legitimacy that recognizes the forms of knowledge we produce which don’t always align with historiographic standards. As Cassidy Sugimoto and I recently wrote, this often comes with journals, societies, and disciplinary realignment.

The least we can do is steer away from a novelty rhetoric, since what is novel often isn’t history, and what is history often isn’t novel.


“Branding” – An Addendum

After writing this post, I read Amardeep Singh’s call to, among other things, avoid branding:

Here’s a way of thinking that might get us past this muddle (and I think I agree with the authors that the hype around DH is a mistake): let’s stop branding our scholarship. We don’t need Next Big Things and we don’t need Academic Superstars, whether they are DH Superstars or Theory Superstars. What we do need is to find more democratic and inclusive ways of thinking about the value of scholarship and scholarly communities.

This is relevant here, and good, but tough to reconcile with the earlier post. In an ideal world, without disciplinary brandings, we can all try to be welcoming of works on their own merits, without relying our preconceived disciplinary criteria. In the present condition, though, it’s tough to see such an environment forming. In that context, maybe a unified digital history “brand” is the best way to stay afloat. This would build barriers against whatever new thing comes along next, though, so it’s a tough question.

Who sits in the 41st chair?

tl;dr Rich-get-richer academic prestige in a scarce job market makes meritocracy impossible. Why some things get popular and others don’t. Also agent-based simulations.

Slightly longer tl;dr This post is about why academia isn’t a meritocracy, at no intentional fault of those in power who try to make it one. None of presented ideas are novel on their own, but I do intend this as a novel conceptual contribution in its connection of disparate threads. Especially, I suggest the predictability of research success in a scarce academic economy as a theoretical framework for exploring successes and failures in the history of science.

But mostly I just beat a “musical chairs” metaphor to death.

Positive Feedback

To the victor go the spoils, and to the spoiled go the victories. Think about it: the Yankees; Alexander the Great; Stanford University. Why do the Yankees have twice as many World Series appearances as their nearest competitors, how was Alex’s empire so fucking vast, and why does Stanford get all the cool grants?

The rich get richer. Enough World Series victories, and the Yankees get the reputation and funding to entice the best players. Ol’ Allie-G inherited an amazing army, was taught by Aristotle, and pretty much every place he conquered increased his military’s numbers. Stanford’s known for amazing tech innovation, so they get the funding, which means they can afford even more innovation, which means even more people think they’re worthy of funding, and so on down the line until Stanford and its neighbors (Google, Apple, etc.) destroy the local real estate market and then accidentally blow up the world.

Alexander's Empire [via]
Alexander’s Empire [via]
Okay, maybe I exaggerated that last bit.

Point is, power begets power. Scientists call this a positive feedback loop: when a thing’s size is exactly what makes it grow larger.

You’ve heard it firsthand when a microphoned singer walks too close to her speaker. First the mic picks up what’s already coming out of the speaker. The mic, doings its job, sends what it hears to an amplifier, sending an even louder version to the very same speaker. The speaker replays a louder version of what it just produced, which is once again received by the microphone, until sound feeds back onto itself enough times to produce the ear-shattering squeal fans of live music have come to dread. This is a positive feedback loop.

Feedback loop. [via]
Feedback loop. [via]
Positive feedback loops are everywhere. They’re why the universe counts logarithmically rather than linearly, or why income inequality is so common in free market economies. Left to their own devices, the rich tend to get richer, since it’s easier to make money when you’ve already got some.

Science and academia are equally susceptible to positive feedback loops. Top scientists, the most well-funded research institutes, and world-famous research all got to where they are, in part, because of something called the Matthew Effect.

Matthew Effect

The Matthew Effect isn’t the reality TV show it sounds like.

For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken even that which he hath. —Matthew 25:29, King James Bible.

It’s the Biblical idea that the rich get richer, and it’s become a popular party trick among sociologists (yes, sociologists go to parties) describing how society works. In academia, the phrase is brought up alongside evidence that shows previous grant-recipients are more likely to receive new grants than their peers, and the more money a researcher has been awarded, the more they’re likely to get going forward.

The Matthew Effect is also employed metaphorically, when it comes to citations. He who gets some citations will accrue more; she who has the most citations will accrue them exponentially faster. There are many correct explanations, but the simplest one will do here: 

If Susan’s article on the danger of velociraptors is cited by 15 other articles, I am more likely to find it and cite her than another article on velociraptors containing the same information, that has never been citedThat’s because when I’m reading research, I look at who’s being cited. The more Susan is cited, the more likely I’ll eventually come across her article and cite it myself, which in turn increases the likelihood that much more that someone else will find her article through my own citations. Continue ad nauseam.

Some of you are thinking this is stupid. Maybe it’s trivially correct, but missing the bigger picture: quality. What if Susan’s velociraptor research is simply better than the competing research, and that’s why it’s getting cited more?

Yes, that’s also an issue. Noticeably awful research simply won’t get much traction. 1 Let’s disqualify it from the citation game. The point is there is lots of great research out there, waiting to be read and built upon, and its quality isn’t the sole predictor of its eventual citation success.

In fact, quality is a mostly-necessary but completely insufficient indicator of research success. Superstar popularity of research depends much more on the citation effects I mentioned above – more citations begets even more. Previous success is the best predictor of future success, mostly independent of the quality of research being shared.

Example of positive feedback loops pushing some articles to citation stardom.
Example of positive feedback loops pushing some articles to citation stardom. [via]
This is all pretty hand-wavy. How do we know success is more important than quality in predicting success? Uh, basically because of Napster.

Popular Music

If VH1 were to produce a retrospective on the first decade of the 21st century, perhaps its two biggest subjects would be illegal music sharing and VH1’s I Love the 19xx… TV series. Napster came and went, followed by LimeWire, eDonkey2000, AudioGalaxy, and other services sued by Metallica. Well-known early internet memes like Hamster Dance and All Your Base Are Belong To Us spread through the web like socially transmitted diseases, and researchers found this the perfect opportunity to explore how popularity worked. Experimentally.

In 2006, a group of Columbia University social scientists designed a clever experiment to test why some songs became popular and others did not, relying on the public interest in online music sharing. They created a music downloading site which gathered 14,341 users, each one to become a participant in their social experiment.

The cleverness arose out of their experimental design, which allowed them to get past the pesky problem of history only ever happening once. It’s usually hard to learn why something became popular, because you don’t know what aspects of its popularity were simply random chance, and what aspects were genuine quality. If you could, say, just rerun the 1960s, changing a few small aspects here or there, would the Beatles still have been as successful? We can’t know, because the 1960s are pretty much stuck having happened as they did, and there’s not much we can do to change it. 2

But this music-sharing site could rerun history—or at least, it could run a few histories simultaneously. When they signed up, each of the site’s 14,341 users were randomly sorted into different groups, and their group number determined how they were presented music. The musical variety was intentionally obscure, so users wouldn’t have heard the bands before.

A user from the first group, upon logging in, would be shown songs in random order, and were given the option to listen to a song, rate it 1-5, and download it. Users from group #2, instead, were shown the songs ranked in order of their popularity among other members of group #2. Group #3 users were shown a similar rank-order of popular songs, but this time determined by the song’s popularity within group #3. So too for groups #4-#9. Every user could listen to, rate, and download music.

Essentially, the researchers put the participants into 9 different self-contained petri dishes, and waited to see which music would become most popular in each. Ranking and download popularity from group #1 was their control group, in that members judged music based on their quality without having access to social influence. Members of groups #2-#9 could be influenced by what music was popular with their peers within the group. The same songs circulated in each petri dish, and each petri dish presented its own version of history.

Music sharing site from Columbia study.
Music sharing site from Columbia study.

No superstar songs emerged out of the control group. Positive feedback loops weren’t built into the system, since popularity couldn’t beget more popularity if nobody saw what their peers were listening to. The other 8 musical petri dishes told a different story, however. Superstars emerged in each, but each group’s population of popular music was very different. A song’s popularity in each group was slightly related to its quality (as judged by ranking in the control group), but mostly it was social-influence-produced chaos. The authors put it this way:

In general, the “best” songs never do very badly, and the “worst” songs never do extremely well, but almost any other result is possible. —Salganik, Dodds, & Watts, 2006

These results became even more pronounced when the researchers increased the visibility of social popularity in the system. The rich got even richer still. A lot of it has to do with timing. In each group, the first few good songs to become popular are the ones that eventually do the best, simply by an accident of circumstance. The first few popular songs appear at the top of the list, for others to see, so they in-turn become even more popular, and so ad infinitum.  The authors go on:

experts fail to predict success not because they are incompetent judges or misinformed about the preferences of others, but because when individual decisions are subject to social influence, markets do not simply aggregate pre-existing individual preferences.

In short, quality is a necessary but insufficient criteria for ultimate success. Social influence, timing, randomness, and other non-qualitative features of music are what turn a good piece of music into an off-the-charts hit.

Wait what about science?

Compare this to what makes a “well-respected” scientist: it ain’t all citations and social popularity, but they play a huge role. And as I described above, simply out of exposure-fueled-propagation, the more citations someone accrues, the more citations they are likely to accrue, until we get a situation like the Yankees (40 world series appearances, versus 20 appearances by the Giants) on our hands. Superstars are born, who are miles beyond the majority of working researchers in terms of grants, awards, citations, etc. Social scientists call this preferential attachment.

Which is fine, I guess. Who cares if scientific popularity is so skewed as long as good research is happening? Even if we take the Columbia social music experiment at face-value, an exact analog for scientific success, we know that the most successful are always good scientists, and the least successful are always bad ones, so what does it matter if variability within the ranks of the successful is so detached from quality?

Except, as anyone studying their #OccupyWallstreet knows, it ain’t that simple in a scarce economy. When the rich get richer, that money’s gotta come from somewhere. Like everything else (cf. the law of conservation of mass), academia is a (mostly) zero-sum game, and to the victors go the spoils. To the losers? Meh.

So let’s talk scarcity.

The 41st Chair

The same guy who who introduced the concept of the Matthew Effect to scientific grants and citations, Robert K. Merton (…of Columbia University), also brought up “the 41st chair” in the same 1968 article.

Merton’s pretty great, so I’ll let him do the talking:

In science as in other institutional realms, a special problem in the workings of the reward system turns up when individuals or organizations take on the job of gauging and suitably rewarding lofty performance on behalf of a large community. Thus, that ultimate accolade in 20th-century science, the Nobel prize, is often assumed to mark off its recipients from all the other scientists of the time. Yet this assumption is at odds with the well-known fact that a good number of scientists who have not received the prize and will not receive it have contributed as much to the advancement of science as some of the recipients, or more.

This can be described as the phenomenon of “the 41st chair.” The derivation of this tag is clear enough. The French Academy, it will be remembered, decided early that only a cohort of 40 could qualify as members and so emerge as immortals. This limitation of numbers made inevitable, of course, the exclusion through the centuries of many talented individuals who have won their own immortality. The familiar list of occupants of this 41st chair includes Descartes, Pascal, Moliere, Bayle, Rousseau, Saint-Simon, Diderot, Stendahl, Flaubert, Zola, and Proust

[…]

But in greater part, the phenomenon of the 41st chair is an artifact of having a fixed number of places available at the summit of recognition. Moreover, when a particular generation is rich in achievements of a high order, it follows from the rule of fixed numbers that some men whose accomplishments rank as high as those actually given the award will be excluded from the honorific ranks. Indeed, their accomplishments sometimes far outrank those which, in a time of less creativity, proved
enough to qualify men for his high order of recognition.

The Nobel prize retains its luster because errors of the first kind—where scientific work of dubious or inferior worth has been mistakenly honored—are uncommonly few. Yet limitations of the second kind cannot be avoided. The small number of awards means that, particularly in times of great scientific advance, there will be many occupants of the 41st chair (and, since the terms governing the award of the prize do not provide for posthumous recognition, permanent occupants of that chair).

Basically, the French Academy allowed only 40 members (chairs) at a time. We can be reasonably certain those members were pretty great, but we can’t be sure that equally great—or greater—women existed who simply never got the opportunity to participate because none of the 40 members died in time.

These good-enough-to-be-members-but-weren’t were said to occupy the French Academy’s 41st chair, an inevitable outcome of a scarce economy (40 chairs) when the potential number benefactors of this economy far outnumber the goods available (40). The population occupying the 41st chair is huge, and growing, since the same number of chairs have existed since 1634, but the population of France has quadrupled in the intervening four centuries.

Returning to our question of “so what if rich-get-richer doesn’t stick the best people at the top, since at least we can assume the people at the top are all pretty good anyway?”, scarcity of chairs is the so-what.

Since faculty jobs are stagnating compared to adjunct work, yet new PhDs are being granted faster than new jobs become available, we are presented with the much-discussed crisis in higher education. Don’t worry, we’re told, academia is a meritocracy. With so few jobs, only the cream of the crop will get them. The best work will still be done, even in these hard times.

Recent Science PhD growth in the U.S. [via]
Recent Science PhD growth in the U.S. [via]
Unfortunately, as the Columbia social music study (among many other studies) showed, true meritocracies are impossible in complex social systems. Anyone who plays the academic game knows this already, and many are quick to point it out when they see people in much better jobs doing incredibly stupid things. What those who point out the falsity of meritocracy often get wrong, however, is intention: the idea that there is no meritocracy because those in power talk the meritocracy talk, but don’t then walk the walk. I’ll talk a bit later about how, even if everyone is above board in trying to push the best people forward, occupants of the 41st chair will still often wind up being more deserving than those sitting in chairs 1-40. But more on that later.

For now, let’s start building a metaphor that we’ll eventually over-extend well beyond its usefulness. Remember that kids’ game Musical Chairs, where everyone’s dancing around a bunch of chairs while the music is playing, but as soon as the music stops everyone’s got to find a chair and sit down? The catch, of course, is that there are fewer chairs than people, so someone always loses when the music stops.

The academic meritocracy works a bit like this. It is meritocratic, to a point: you can’t even play the game without proving some worth. The price of admission is a Ph.D. (which, granted, is more an endurance test than an intelligence test, but academic success ain’t all smarts, y’know?), a research area at least a few people find interesting and believe you’d be able to do good work in it, etc. It’s a pretty low meritocratic bar, since it described 50,000 people who graduated in the U.S. in 2008 alone, but it’s a bar nonetheless. And it’s your competition in Academic Musical Chairs.

Academic Musical Chairs

Time to invent a game! It’s called Academic Musical Chairs, the game where everything’s made up and the points don’t matter. It’s like Regular Musical Chairs, but more complicated (see Fig. 1). Also the game is fixed.

Figure 1: Academic Musical Chairs
Figure 1: Academic Musical Chairs

See those 40 chairs in the middle green zone? People sitting in them are the winners. Once they’re seated they have what we call in the game “tenure”, and they don’t get up until they die or write something controversial on twitter. Everyone bustling around them, the active players, are vying for seats while they wait for someone to die; they occupy the yellow zone we call “the 41st chair”. Those beyond that, in the red zone, can’t yet (or may never) afford the price of game admission; they don’t have a Ph.D., they already said something controversial on Twitter, etc. The unwashed masses, you know?

As the music plays, everyone in the 41st chair is walking around in a circle waiting for someone to die and the music to stop. When that happens, everyone rushes to the empty seat. A few invariably reach it simultaneously, until one out-muscles the others and sits down. The sitting winner gets tenure. The music starts again, and the line continues to orbit the circle.

If a player spends too long orbiting in the 41st chair, he is forced to resign. If a player runs out of money while orbiting, she is forced to resign. Other factors may force a player to resign, but they will never appear in the rulebook and will always be a surprise.

Now, some players are more talented than others, whether naturally or through intense training. The game calls this “academic merit”, but it translates here to increased speed and strength, which helps some players reach the empty chair when the music stops, even if they’re a bit further away. The strength certainly helps when competing with others who reach the chair at the same time.

A careful look at Figure 1 will reveal one other way players might increase their chances of success when the music stops. The 41st chair has certain internal shells, or rings, which act a bit like that fake model of an atom everyone learned in high-school chemistry. Players, of course, are the electrons.

Electron shells. [via]
Electron shells. [via]
You may remember that the further out the shell, the more electrons can occupy it(-ish): the first shell holds 2 electrons, the second holds 8; third holds 18; fourth holds 32; and so on. The same holds true for Academic Musical Chairs: the coveted interior ring only fits a handful of players; the second ring fits an order of magnitude more; the third ring an order of magnitude more than that, and so on.

Getting closer to the center isn’t easy, and it has very little to do with your “academic rigor”! Also, of course, the closer you are to the center, the easier it is to reach either the chair, or the next level (remember positive feedback loops?). Contrariwise, the further you are from the center, the less chance you have of ever reaching the core.

Many factors affect whether a player can proceed to the next ring while the music plays, and some factors actively count against a player. Old age and being a woman, for example, take away 1 point. Getting published or cited adds points, as does already being friends with someone sitting in a chair (the details of how many points each adds can be found in your rulebook). Obviously the closer you are to the center, the easier you can make friends with people in the green core, which will contribute to your score even further. Once your score is high enough, you proceed to the next-closest shell.

Hooray, someone died! Let’s watch what happens.

The music stops. The people in the innermost ring who have the luckiest timing (thus are closest to the empty chair) scramble for it, and a few even reach it. Some very well-timed players from the 2nd & 3rd shells also reach it, because their “academic merit” has lent them speed and strength to reach past their position. A struggle ensues. Miraculously, a pregnant black woman sits down (this almost never happens), though not without some bodily harm, and the music begins again.

Oh, and new shells keep getting tacked on as more players can afford the cost of admission to the yellow zone, though the green core remains the same size.

Bizarrely, this is far from the first game of this nature. A Spanish boardgame from 1587 called the Courtly Philosophy had players move figures around a board, inching closer to living a luxurious life in the shadow of a rich patron. Random chance ruled their progression—a role of the dice—and occasionally they’d reach a tile that said things like: “Your patron dies, go back 5 squares”.

The courtier's philosophy. [via]
The courtier’s philosophy. [via]
But I digress. Let’s temporarily table the scarcity/41st-chair discussion and get back to the Matthew Effect.

The View From Inside

A friend recently came to me, excited but nervous about how well they were being treated by their department at the expense of their fellow students. “Is this what the Matthew Effect feels like?” they asked. Their question is the reason I’m writing this post, because I spent the next 24 hours scratching my head over “what does the Matthew Effect feel like?”.

I don’t know if anyone’s looked at the psychological effects of the Matthew Effect (if you do, please comment?), but my guess is it encompasses two feelings: 1) impostor syndrome, and 2) hard work finally paying off.

Since almost anyone who reaps the benefits of the Matthew Effect in academia will be an intelligent, hard-working academic, a windfall of accruing success should feel like finally reaping the benefits one deserves. You probably realize that luck played a part, and that many of your harder-working, smarter friends have been equally unlucky, but there’s no doubt in your mind that, at least, your hard work is finally paying off and the academic community is beginning to recognize that fact. No matter how unfair it is that your great colleagues aren’t seeing the same success.

But here’s the thing. You know how in physics, gravity and acceleration feel equivalent? How, if you’re in a windowless box, you wouldn’t be able to tell the difference between being stationary on Earth, or being pulled by a spaceship at 9.8 m/s2 through deep space? Success from merit or from Matthew Effect probably acts similarly, such that it’s impossible to tell one from the other from the inside.

Gravity vs. Acceleration. [via]
Gravity vs. Acceleration. [via]
Incidentally, that’s why the last advice you ever want to take is someone telling you how to succeed from their own experience.

Success

Since we’ve seen explosive success requires but doesn’t rely on skill, quality, or intent, the most successful people are not necessarily in the best position to understand the reason for their own rise. Their strategies may have paid off, but so did timing, social network effects, and positive feedback loops. The question you should be asking is, why didn’t other people with the same strategies also succeed?

Keep this especially in mind if you’re a student, and your tenured-professor advised you to seek an academic career. They may believe that giving you their strategies for success will help you succeed, when really they’re just giving you one of 50,000 admission tickets to Academic Musical Chairs.

Building a Meritocracy

I’m teetering well-past the edge of speculation here, but I assume the communities of entrenched academics encouraging undergraduates into a research career are the same communities assuming a meritocracy is at play, and are doing everything they can in hiring and tenure review to ensure a meritocratic playing field.

But even if gender bias did not exist, even if everyone responsible for decision-making genuinely wanted a meritocracy, even if the game weren’t rigged at many levels, the economy of scarcity (41st chair) combined with the Matthew Effect would ensure a true meritocracy would be impossible. There are only so many jobs, and hiring committees need to choose some selection criteria; those selection criteria will be subject to scarcity and rich-get-richer effects.

I won’t prove that point here, because original research is beyond the scope of this blog post, but I have a good idea of how to do it. In fact, after I finish writing this, I probably will go do just that. Instead, let me present very similar research, and explain how that method can be used to answer this question.

We want an answer to the question of whether positive feedback loops and a scarce economy are sufficient to prevent the possibility of a meritocracy. In 1971, Tom Schelling asked an unrelated question which he answered using a very relevant method: can racial segregation manifest in a community whose every actor is intent on not living a segregated life? Spoiler alert: yes.

He answered this question using by simulating an artificial world—similar in spirit to the Columbia social music experiment, except for using real participants, he experimented on very simple rule-abiding game creatures of his own invention. A bit like having a computer play checkers against itself.

The experiment is simple enough: a bunch of creatures occupy a checker board, and like checker pieces, they’re red or black. Every turn, one creature has the opportunity to move randomly to another empty space on the board, and their decision to move is based on their comfort with their neighbors. Red pieces want red neighbors, and black pieces want black neighbors, and they keep moving randomly ’till they’re all comfortable. Unsurprisingly, segregated creature communities appear in short order.

What if we our checker-creatures were more relaxed in their comforts? They’d be comfortable as long as they were in the majority; say, at least 50% of their neighbors were the same color. Again, let the computer play itself for a while, and within a few cycles the checker board is once again almost completely segregated.

Schelling segregation. [via]
Schelling segregation. [via]
What if the checker pieces are excited about the prospect of a diverse neighborhood? We relax the criteria even more, so red checkers only move if fewer than a third of their neighbors are red (that is, they’re totally comfortable with 66% of their neighbors being black)? If we run the experiment again, we see, again, the checker board breaks up into segregated communities.

Schelling’s claim wasn’t about how the world worked, but about what the simplest conditions were that could still explain racism. In his fictional checkers-world, every piece could be generously interested in living in a diverse neighborhood, and yet the system still eventually resulted in segregation. This offered a powerful support for the theory that racism could operate subtly, even if every actor were well-intended.

Vi Hart and Nicky Case created an interactive visualization/game that teaches Schelling’s segregation model perfectly. Go play it. Then come back. I’ll wait.


Such an experiment can be devised for our 41st-chair/positive-feedback system as well. We can even build a simulation whose rules match the Academic Musical Chairs I described above. All we need to do is show that a system in which both effects operate (a fact empirically proven time and again in academia) produces fundamental challenges for meritocracy. Such a model would be show that simple meritocratic intent is insufficient to produce a meritocracy. Hulk smashing the myth of the meritocracy seems fun; I think I’ll get started soon.

The Social Network

Our world ain’t that simple. For one, as seen in Academic Musical Chairs, your place in the social network influences your chances of success. A heavy-hitting advisor, an old-boys cohort, etc., all improve your starting position when you begin the game.

To put it more operationally, let’s go back to the Columbia social music experiment. Part of a song’s success was due to quality, but the stuff that made stars was much more contingent on chance timing followed by positive feedback loops. Two of the authors from the 2006 study wrote another in 2007, echoing this claim that good timing was more important than individual influence:

models of information cascades, as well as human subjects experiments that have been designed to test the models (Anderson and Holt 1997; Kubler and Weizsacker 2004), are explicitly constructed such that there is nothing special about those individuals, either in terms of their personal characteristics or in their ability to influence others. Thus, whatever influence these individuals exert on the collective outcome is an accidental consequence of their randomly assigned position in the queue.

These articles are part of a large literature in predicting popularity, viral hits, success, and so forth. There’s The Pulse of News in Social Media: Forecasting Popularity by Bandari, Asur, & Huberman, which showed that a top predictor of newspaper shares was the source rather than the content of an article, and that a major chunk of articles that do get shared never really make it to viral status. There’s Can Cascades be Predicted? by Cheng, Adamic, Dow, Kleinberg, and Leskovec (all-star cast if ever I saw one), which shows the remarkable reliance on timing & first impressions in predicting success, and also the reliance on social connectivity. That is, success travels faster through those who are well-connected (shocking, right?), and structural properties of the social network are important. This study by Susarla et al. also shows the importance of location in the social network in helping push those positive feedback loops, effecting the magnitude of success in YouTube Video shares.

Twitter information cascade. [via]
Twitter information cascade. [via]
Now, I know, social media success does not an academic career predict. The point here, instead, is to show that in each of these cases, before sharing occurs and not taking into account social media effects (that is, relying solely on the merit of the thing itself), success is predictable, but stardom is not.

Concluding, Finally

Relating it to Academic Musical Chairs, it’s not too difficult to say whether someone will end up in the 41st chair, but it’s impossible to tell whether they’ll end up in seats 1-40 until you keep an eye on how positive feedback loops are affecting their career.

In the academic world, there’s a fertile prediction market for Nobel Laureates. Social networks and Matthew Effect citation bursts are decent enough predictors, but what anyone who predicts any kind of success will tell you is that it’s much easier to predict the pool of recipients than it is to predict the winners.

Take Economics. How many working economists are there? Tens of thousands, at least. But there’s this Econometric Society which began naming Fellows in 1933, naming 877 Fellows by 2011. And guess what, 60 of 69 Nobel Laureates in Economics before 2011 were Fellows of the society. The other 817 members are or were occupants of the 41st chair.

The point is (again, sorry), academic meritocracy is a myth. Merit is a price of admission to the game, but not a predictor of success in a scarce economy of jobs and resources. Once you pass the basic merit threshold and enter the 41st chair, forces having little to do with intellectual curiosity and rigor guide eventual success (ahem). Small positive biases like gender, well-connected advisors, early citations, lucky timing, etc. feed back into increasingly larger positive biases down the line. And since there are only so many faculty jobs out there, these feedback effects create a naturally imbalanced playing field. Sometimes Einsteins do make it into the middle ring, and sometimes they stay patent clerks. Or adjuncts, I guess. Those who do make it past the 41st chair are poorly-suited to tell you why, because by and large they employed the same strategies as everybody else.

Figure 1: Academic Musical Chairs
Yep, Academic Musical Chairs

And if these six thousand words weren’t enough to convince you, I leave you with this article and this tweet. Have a nice day!

Addendum for Historians

You thought I was done?

As a historian of science, this situation has some interesting repercussions for my research. Perhaps most importantly, it and related concepts from Complex Systems research offer a middle ground framework between environmental/contextual determinism (the world shapes us in fundamentally predictable ways) and individual historical agency (we possess the power to shape the world around us, making the world fundamentally unpredictable).

More concretely, it is historically fruitful to ask not simply what non-“scientific” strategies were employed by famous scientists to get ahead (see Biagioli’s Galileo, Courtier), but also what did or did not set those strategies apart from the masses of people we no longer remember. Galileo, Courtier provides a great example of what we historians can do on a larger scale: it traces Galileo’s machinations to wind up in the good graces of a wealthy patron, and how such a system affected his own research. Using recently-available data on early modern social and scholarly networks, as well as the beginnings of data on people’s activities, interests, practices, and productions, it should be possible to zoom out from Biagioli’s viewpoint and get a fairly sophisticated picture of trajectories and practices of people who weren’t Galileo.

This is all very preliminary, just publicly blogging whims, but I’d be fascinated by what a wide-angle (dare I say, macroscopic?) analysis of the 41st chair in could tell us about how social and “scientific” practices shaped one another in the 16th and 17th centuries. I believe this would bear previously-impossible fruit, since a lone historian grasping ten thousand tertiary actors at once is a fool’s errand, but is a walk in the park for my laptop.

As this really is whim-blogging, I’d love to hear your thoughts.

Notes:

  1. Unless it’s really awful, but let’s avoid that discussion here.
  2. short of a TARDIS.

Connecting the Dots

This is the incredibly belated transcript of my HASTAC 2015 keynote. Many thanks to the organizers for inviting me, and to my fellow participants for all the wonderful discussions. The video and slides are also online. You can find citations to some of the historical illustrations and many of my intellectual inspirations here. What I said and what I wrote probably don’t align perfectly.

When you’re done reading this, you should read Roopika Risam’s closing keynote, which connects surprisingly well with this, though we did not plan it.


If you take a second to expand and disentangle “HASTAC”, you get a name of an organization that doubles as a fairly strong claim about the world: that Humanities, Arts, Science, and Technology are separate things, that they probably aren’t currently in alliance with one another, and that they ought to form an alliance.

This intention is reinforced in the theme of this year’s conference: “The Art and Science of Digital Humanities.” Here again we get the four pillars: humanities, arts, science, and technology. In fact, bear with me as I read from the CFP:

We welcome sessions that address, exemplify, and interrogate the interdisciplinary nature of DH work. HASTAC 2015 challenges participants to consider how the interplay of science, technology, social sciences, humanities, and arts are producing new forms of knowledge, disrupting older forms, challenging or reifying power relationships, among other possibilities.

Here again is that implicit message: disciplines are isolated, and their interplay can somehow influence power structures. As with a lot of digital humanities and cultural studies, there’s also a hint of activism: that building intentional bridges is a beneficial activity, and we’re organizing the community of HASTAC around this goal.

hastac-outline

This is what I’ll be commenting on today. First, what does disciplinary isolation mean? I put this historically, and argue that we must frame disciplinary isolation in a rhetorical context.

This brings me to my second point about ontology. It turns out the way we talk about isolation is deeply related to the way we think about knowledge, the way we illustrate it, and ultimately the shape of knowledge itself. That’s ontology.

My third point brings us back to HASTAC: that we represent an intentional community, and this intent is to build bridges which positively affect the academy and the world.

I’ll connect these three strands by arguing that we need a map to build bridges, and we need to carefully think about the ontology of knowledge to draw that map. And once we have a map, we can use it to design a better territory.

In short, this plenary is a call-to-action. It’s my vocal support for an intentionally improved academy, my exploration of its historical and rhetorical underpinnings, and my suggestions for affecting positive change in the future.

PhDKnowledge.002[1]
Matt Might’s Illustrated Guide to the Ph.D.
Let’s begin at the beginning. With isolation.

Stop me if you’ve heard this one before:

Within this circle is the sum of all human knowledge. It’s nice, it’s enclosed, it’s bounded. It’s a comforting thought, that everything we’ve ever learned or created sits comfortably inside these boundaries.

This blue dot is you, when you’re born. It’s a beautiful baby picture. You’ve got the whole world ahead of you, an entire universe to learn, just waiting. You’re at the center because you have yet to reach your proverbial hand out in any direction and begin to learn.

Matt Might's Illustrated Guide to the Ph.D.
Matt Might’s Illustrated Guide to the Ph.D.

But time passes and you grow. You go to highschool, you take your liberal arts and sciences, and you slowly expand your circle into the great known. Rounding out your knowledge, as it were.

Then college happens! Oh, those heady days of youth. We all remember it, when the shape of our knowledge started leaning tumorously to one side. The ill-effects of specialization and declaring a major, I suspect.

As you complete a master’s degree, your specialty pulls your knowledge inexorably towards the edge of the circle of the known. You’re not a jack of all trades anymore. You’re an expert.

http://matt.might.net/articles/phd-school-in-pictures/
Matt Might’s Illustrated Guide to the Ph.D.

Then your PhD advisor yells at you to focus and get even smaller. So you complete your qualifying exams and reach the edge of what’s known. What lies beyond the circle? Let’s zoom in and see!

Matt Might's Illustrated Guide to the Ph.D.
Matt Might’s Illustrated Guide to the Ph.D.

You’ve reached the edge. The end of the line. The sum of all human knowledge stops here. If you want to go further, you’ll need to come up with something new. So you start writing your dissertation.

That’s your PhD. Right there, at the end of the little arrow.

You did it. Congratulations!

You now know more about less than anybody else in the world. You made a dent in the circle, you pushed human knowledge out just a tiny bit further, and all it cost you was your mental health, thirty years of your life, and the promise of a certain future. …Yay?

PhDKnowledge.012[1]
Matt Might’s Illustrated Guide to the Ph.D.
So here’s the new world that you helped build, the new circle of knowledge. With everyone in this room, I bet we’ve managed to make a lot of dents. Maybe we’ve even managed to increase the circle’s radius a bit!

Now, what I just walked us all through is Matt Might’s illustrated guide to the Ph.D. It made its rounds on the internet a few years back, it was pretty popular.

And, though I’m being snarky about it, it’s a pretty uplifting narrative. It provides that same dual feeling of insignificance and importance that you get when you stare at the Hubble Ultra Deep Field. You know the picture, right?

Hubble Ultra Deep Field
Hubble Ultra Deep Field

There are 10,000 galaxies on display here, each with a hundred billion stars. To think that we, humans, from our tiny vantage point on Earth, could see so far and so much because of the clever way we shape glass lenses? That’s really cool.

And saying that every pinprick of light we see is someone else’s PhD? Well, that’s a pretty fantastic metaphor. Makes getting the PhD seem worth it, right?

Dante and the Early Astronomers; M. A. Orr (Mrs. John Evershed), 1913
Dante and the Early Astronomers; M. A. Orr (Mrs. John Evershed), 1913

It kinda reminds me of the cosmological theories of some of our philosophical ancestors.

The cosmos (Greek for “Order”), consisted of concentric, perfectly layered spheres, with us at the very center.

The cosmos was bordered by celestial fire, the light from heaven, and stars were simply pin-pricks in a dark curtain which let the heavenly light shine through.

Flammarion
Flammarion

So, if we beat Matt Might’s PhD metaphor to death, each of our dissertations are poking holes in the cosmic curtain, letting the light of heaven shine through. And that’s a beautiful thought, right? Enough pinpricks, and we’ll all be bathed in light.

Expanding universe.
Expanding universe.

But I promised we’d talk about isolation, and even if we have to destroy this metaphor to get there, we’ll get there.

The universe is expanding. That circle of knowledge we’re pushing the boundaries of? It’s getting bigger too. And as it gets larger, things that were once close get further and further apart. You and I and Alpha Centauri were all neighbors for the big bang, but things have changed since then, and the star that was once our neighbor is now 5 light years away.

Atlas of Science, Katy Borner (2010).
Atlas of Science, Katy Borner (2010).

In short, if we’re to take Matt Might’s PhD model as accurate, then the result of specialization is inexorable isolation. Let’s play this out.

Let’s say two thousand years ago, a white dude from Greece invented science. He wore a beard.

[Note for readers: the following narrative is intentionally awful. Read on and you’ll see why.]

Untitled-3

He and his bearded friends created pretty much every discipline we’re familiar with at Western universities: biology, cosmology, linguistics, philosophy, administration, NCAA football, you name it.

Over time, as Ancient Greek beards finished their dissertations, the boundaries of science expanded in every direction. But the sum of human knowledge was still pretty small back then, so one beard could write many dissertations, and didn’t have to specialize in only one direction. Polymaths still roamed the earth.

Untitled-3

Fast forward a thousand years or so. Human knowledge had expanded in the interim, and the first European universities branched into faculties: theology, law, medicine, arts.

Another few hundred years, and we’ve reached the first age of information overload. It’s barely possible to be a master of all things, and though we remember scholars and artists known for their amazing breadth, this breadth is becoming increasingly difficult to manage.

We begin to see the first published library catalogs, since the multitude of books required increasingly clever and systematic cataloging schemes. If you were to walk through Oxford in 1620, you’d see a set of newly-constructed doors with signs above them denoting their disciplinary uses: music, metaphysics, history, moral philosophy, and so on.

The encyclopedia of Diderot & D'alembert
The encyclopedia of Diderot & D’alembert

Time goes on a bit further, the circle of knowledge expands, and specialization eventually leads to fracturing.

We’ve reached the age of these massive hierarchical disciplinary schemes, with learning branching in every direction. Our little circle has become unmanageable.

A few more centuries pass. Some German universities perfect the art of specialization, and they pass it along to everyone else, including the American university system.

Within another 50 years, CP Snow famously invoked the “Two Cultures” of humanities and sciences.

And suddenly here we are

Untitled-3

On the edge of our circle, pushing outward, with every new dissertation expanding our radius, and increasing the distance to our neighbors.

Basically, the inevitable growth of knowledge results in an equally inevitable isolation. This is the culmination of super-specialization: a world where the gulf between disciplines is impossible to traverse, filled with language barriers, value differences, and intellectual incommensurabilities. You name it.

hastac-outline

By this point, 99% of the room is probably horrified. Maybe it’s by the prospect of an increasingly isolated academy. More likely the horror’s at my racist, sexist, whiggish, Eurocentric account of the history of science, or at my absurdly reductivist and genealogical account of the growth of knowledge.

This was intentional, and I hope you’ll forgive me, because I did it to prove a point: the power of visual rhetoric in shaping our thoughts. We use the word “imagine” to describe every act of internal creation, whether or not it conforms to the root word of “image”. In classical and medieval philosophy, thought itself was a visual process, and complex concepts were often illustrated visually in order to help students understand and remember. Ars memoriae, it was called.

And in ars memoriae, concepts were not only given visual form, they were given order. This order wasn’t merely a clever memorization technique, it was a reflection on underlying truths about the relationship between concepts. In a sense, visual representations helped bridge human thought with divine structure.

This is our entrance into ontology. We’ve essentially been talking about interdisciplinarity for two thousand years, and always alongside a visual rhetoric about the shape, or ontology, of knowledge. Over the next 10 minutes, I’ll trace the interwoven histories of ontology, illustrations, and rhetoric of interdisciplinarity. This will help contextualize our current moment, and the intention behind meeting at a conference like this one. It should, I hope, also inform how we design our community going forward.

Let’s take a look some alternatives to the Matt Might PhD model.

Diagrams of Knowledge
Diagrams of Knowledge

Countless cultural and religious traditions associate knowledge with trees; indeed, in the Bible, the fruit of one tree is knowledge itself.

During the Roman Empire and the Middle Ages, the sturdy metaphor of trees provided a sense of lineage and order to the world that matched perfectly with the neatly structured cosmos of the time. Common figures of speech we use today like “the root of the problem” or “branches of knowledge” betray the strength with which we connected these structures to one another. Visual representations of knowledge, obviously, were also tree-like.

See, it’s impossible to differentiate the visual from the essential here. The visualization wasn’t a metaphor, it was an instantiation of essence. There are three important concepts that link knowledge to trees, which at that time were inseparable.

One: putting knowledge on a tree implied a certain genealogy of ideas. What we discovered and explored first eventually branched into more precise subdisciplines, and the history of those branches are represented on the tree. This is much like any family tree you or I would put together with our parents and grandparents and so forth. The tree literally shows the historical evolution of concepts.

Two: putting knowledge on a tree implied a specific hierarchy that would by the Enlightenment become entwined with how we understood the universe. Philosophy separates into the theoretical and the practical; basic math into geometry and arithmetic. This branching hierarchy gave an importance to the root of the tree, be that root physics or God or philosophy or man, and that importance decreased as you reached the further limbs. It also implied an order of necessity: the branches of math could not exist without the branch of philosophy it stemmed from. This is why today people still think things like physics is the most important discipline.

Three: As these trees were represented, there was no difference between the concept of a branch of knowledge, the branch of knowledge itself, and the object of study of that branch of knowledge. The relationship of physics to chemistry isn’t just genealogical or foundational; it’s actually transcendent. The conceptual separation of genealogy, ontology, and transcendence would not come until much later.

It took some time for the use of the branching tree as a metaphor for knowledge to take hold, competing against other visual and metaphorical representations, but once it did, it ruled victorious for centuries. The trees spread and grew until they collapsed under their own weight by the late nineteenth century, leaving a vacuum to be filled by faceted classification systems and sprawling network visualizations. The loss of a single root as the source of knowledge signaled an epistemic shift in how knowledge is understood, the implications of which are still unfolding in present-day discussions of interdisciplinarity.

By visualizing knowledge itself as a tree, our ancestors reinforced both an epistemology and a phenomenology of knowledge, ensuring that we would think of concepts as part of hierarchies and genealogies for hundreds of years. As we slowly moved away from strictly tree-based representations of knowledge in the last century, we have also moved away from the sense that knowledge forms a strict hierarchy. Instead, we now believe it to be a diffuse system of occasionally interconnected parts.

Of course, the divisions of concepts and bodies of study have no natural kind. There are many axes against which we may compare biology to literature, but even the notion of an axis of comparison implies a commonality against which the two are related which may not actually exist. Still, we’ve found the division of knowledge into subjects, disciplines, and fields a useful practice since before Aristotle. The metaphors we use for these divisions influence our understanding of knowledge itself: structured or diffuse; overlapping or separate; rooted or free; fractals or divisions; these metaphors inform how we think about thinking, and they lend themselves to visual representations which construct and reinforce our notions of the order of knowledge.

Arbor Scientiae, late thirteenth century, Ramon Llull. [via]
Arbor Scientiae, late thirteenth century, Ramon Llull.
Given all this, it should come as no surprise that medieval knowledge was shaped like a tree – God sat at the root, and the great branching of knowledge provided a transcendental order of things. Physics, ethics, and biology branched further and further until tiny subdisciplines sat at every leaf. One important aspect of these illustrations was unity – they were whole and complete, and even more, they were all connected. This mirrors pretty closely that circle from Matt Might.

Christophe de Savigny’s Tableaux: Accomplis de tous les arts liberaux, 1587
Christophe de Savigny’s Tableaux: Accomplis de tous les arts liberaux, 1587

Speaking of that circle I had up earlier, many of these branching diagrams had a similar feature. Notice the circle encompassing this illustration, especially the one on the left here: it’s a chain. The chain locks the illustration down: it says, there are no more branches to grow.

This and similar illustrations were also notable for their placement. This was an index to a book, an early encyclopedia of sorts – you use the branches to help you navigate through descriptions of the branches of knowledge. How else should you organize a book of knowledge than by its natural structure?

Bacon's Advancement of Learning
Bacon’s Advancement of Learning

We start seeing some visual, rhetorical, and ontological changes by the time of Francis Bacon, who wrote “the distributions and partitions of knowledge are […] like branches of a tree that meet in a stem, which hath a dimension and quantity of entireness and continuance, before it come to discontinue and break itself into arms and boughs.”

The highly influential book broke the trends in three ways:

  1. it broke the “one root” model of knowledge.
  2. It shifted the system from closed to open, capable of growth and change
  3. it detached natural knowledge from divine wisdom.

Bacon’s uprooting of knowledge, dividing it into history, poesy, and philosophy, each with its own root, was an intentional rhetorical strategy. He used it to argue that natural philosophy should be explored at the expense of poesy and history. Philosophy, what we now call science, was now a different kind of knowledge, worthier than the other two.

And doesn’t that feel a lot like today?

Bacon’s system also existed without an encompassing chain, embodying the idea that learning could be advanced; that the whole of knowledge could not be represented as an already-grown tree. There was no complete order of knowledge, because knowledge changes.

And, by being an imperfect, incomplete entity, without union, knowledge was notably separated from divine wisdom.

Kircher's Philosophical tree representing all branches of knowledge, from Ars Magna Sciendi (1669), p. 251.
Kircher’s Philosophical tree representing all branches of knowledge, from Ars Magna Sciendi (1669), p. 251.

Of course, divinity and transcendence wasn’t wholly exorcised from these ontological illustrations: Athanasius Kircher put God on the highest branch, feeding the tree’s growth. (Remember, from my earlier circle metaphor, the importance of the poking holes in the fabric of the cosmos to let the light of heaven shine through?). Descartes as well continued to describe knowledge as a tree, whose roots were reliant on divine existence.

Chambers' Cyclopædia
Chambers’ Cyclopædia

But even without the single trunk, without God, without unity, the metaphors were still ontologically essential, even into the 18th century. This early encyclopedia by Ephraim Chambers uses the tree as an index, and Chambers writes:

“the Origin and Derivation of the several Parts, and the relation in which [the disciplines] stand to their common Stock and to each other; will assist in restoring ‘em to their proper Places

Their proper places. This order is still truth with a capital T.

The encyclopedia of Diderot & D'alembert
The encyclopedia of Diderot & D’alembert

It wasn’t until the mid-18th century, with Diderot and d’Alembert’s encyclopedia, that serious thinkers started actively disputing the idea that these trees were somehow indicative of the essence of knowledge. Even they couldn’t escape using trees, however, introducing their enyclopedia by saying “We have chosen a division which has appeared to us most nearly satisfactory for the encyclopedia arrangement of our knowledge and, at the same time, for its genealogical arrangement.

Even if the tree wasn’t the essence of knowledge, it still represented possible truth about the genealogy of ideas. It took until a half century later, with the Encyclopedia Britannica, for the editors to do away with tree illustrations entirely and write that the world was “perpetually blended in almost every branch of human knowledge”. (Notice they still use the word branch.) By now, a philosophical trend that began with Bacon was taking form through the impossibility of organizing giant libraries and encyclopedia: that there was no unity of knowledge, no implicit order, and no viable hierarchy.

Banyan tree [via]
It took another century to find a visual metaphor to replace the branching tree. Herbert Spencer wrote that the branches of knowledge “now and again re-unite […], they severally send off and receive connecting growths; and the intercommunion is ever becoming more frequent, more intricate, more widely ramified.” Classification theorist S.R. Ranganathan compared knowledge to the Banyan tree from his home country of India, which has roots which both grow from the bottom up and the top down.

Otlet 1937
Otlet 1937

The 20th century saw a wealth of new shapes of knowledge. Paul Otlet conceived a sort of universal network, connected through individual’s thought processes. H.G. Wells shaped knowledge very similar to Matt Might’s illustrated PhD from earlier: starting with a child’s experience of learning and branching out. These were both interesting developments, as they rhetorically placed the ontology of knowledge in the realm of the psychological or the social: driven by people rather than some underlying objective reality about conceptual relationships.

Porter’s 1939 Map of Physics [via]
Around this time there was a flourishing of visual metaphors, to fill the vacuum left by the loss of the sturdy tree.There was, uncoincidentally, a flourishing of uses for these illustrations. Some, like this map, was educational and historical, teaching students how the history of physics split and recombined like water flowing through rivers and tributaries. Others, like the illustration to the right, showed how the conceptual relationships between knowledge domains differed from and overlapped with library classification schemes and literature finding aids.

Small & Garfield, 1985
Small & Garfield, 1985

By the 80s, we start seeing a slew of the illustrations we’re all familiar with: those sexy sexy network spaghetti-and-meatball graphs. We often use them to illustrate citation chains, and the relationship between academic disciplines. These graphs, so popular in the 21st century, go hand-in-hand with the ontological baggage we’re used to: that knowledge is complex, unrooted, interconnected, and co-constructed. This fits well with the current return to a concept we’d mostly left in the 19th century: that knowledge is a single, growing unit, that it’s consilient, that everyone is connected. It’s a return to the Republic of Letters from the C.P. Snow’s split of the Two Cultures.

It also notably departs from genealogical, transcendental, and even conceptual discussions of knowledge. These networks, broadly construed, are social representations, and while those relationships may often align with conceptual ones, concepts are not what drive the connections.

Fürbringer's Illustration of Bird Evolution, 1888
Fürbringer’s Illustration of Bird Evolution, 1888

Interestingly, there is precedent in these sorts of illustrations in the history of evolutionary biology. In the late 19th-century, illustrators and scientists began asking what it would look like if you took a slice from the evolutionary tree – or, what does the tree of life look like when you’re looking at it from the top-down?

What you get is a visual structure very similar to the network diagrams we’re now used to. And often, if you probe those making the modern visualizations, they will weave a story about the history of these networks that is reminiscent of branching evolutionary trees.

There’s another set of epistemological baggage that comes along with these spaghetti-and-meatball-graphs. Ben Fry, a well-known researcher in information visualization, wrote:

“There is a tendency when using [networks] to become smitten with one’s own data. Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is ‘complex’ fits just fine with the creator’s own interpretation of it. Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.”

Actually, were any of you here at last night’s Pink Floyd light show in the planetarium? They’re a lot like that. [Yes, readers, HASTAC put on a Pink Floyd light show.]

And this is where we are now.

hastac-outline

Which brings us back to the outline, and HASTAC. Cathy Davidson has often described HASTAC as a social network, which is (at least on the web) always an intentionally-designed medium. Its design grants certain affordances to users: is it easier to communicate individually or in groups? What types of communities, events, or content is prioritized? These are design decisions that affect how the HASTAC community functions and interacts.

And the design decisions going into HASTAC are informed by its intent, so what is that intent? In their groundbreaking 2004 manifesto in the Chronicle, Cathy Davidson and David Goldberg wrote:

“We believe that a new configuration in the humanities must be championed to ensure their centrality to all intellectual enterprises in the university and, more generally, to understanding the human condition and thereby improving it; and that those intellectual changes must be supported by new institutional structures and values.”

This was a HASTAC rallying cry: how can the humanities constructively inform the world? Notice especially how they called for “New Institutional Structures.”

Remember earlier, how I talked about the problem if isolation? While my story about it was problematic, it doesn’t make disciplinary superspecialization any less real a problem. For all its talk of interdisciplinarity, academia is averse to synthesis on many fronts, superspecialization being just one of them. A dissertation based on synthesis, for example, is much less likely to get through a committee than a thorough single intellectual contribution to one specific field.

The academy is also weirdly averse to writing for public audiences. Popular books won’t get you tenure. But every discipline is a popular audience to most other disciplines: you wouldn’t talk to a chemist about history the same way you’d talk to a historian. Synthetic and semi-public work is exactly the sort of work that will help with HASTAC’s goal of a truly integrated and informed academy for social good, but the cards are stacked against it. Cathy and David hit the nail on the head when they target institutional structures as a critical point for improvement.

This is where design comes in.

Richmond, 1954
Richmond, 1954

Recall again the theme this year: The Art and Science of Digital Humanities. I propose we take the next few days to think about how we can use art and science to make HASTAC even better at living up its intent. That is, knowing what we do about collaboration, about visual rhetoric, about the academy, how can we design an intentional community to meet its goals? Perusing the program, it looks like most of us will already be discussing exactly this, but it’s useful to put a frame around it.

When we talk about structure and the social web, there’s many great examples we may learn from. One such example is that of Tara McPherson and her colleagues, in designing the web publishing platform Scalar. As opposed to WordPress, its cousin in functionality, Scalar was designed with feminist and humanist principles in mind, allowing for more expressive, non-hierarchical “pathways” through content.

When talking of institutional, social, and web-based structures, we can also take lessons history. In Early Modern Europe, the great network of information exchange known as the Republic of Letters was a shining example of the influence of media structures on innovation. Scholars would often communicate through “hubs”, which were personified in people nicknamed things like “the mailbox of Europe”. And they helped distribute new research incredibly efficiently through their vast web of social ties. These hubs were essential to what’s been called the scientific revolution, and without their structural role, it’s unlikely you’d see references to a scientific revolution in the 17th century Europe.

Similarly, at that time, the Atlantic slave trade was wreaking untold havoc on the world. For all the ills it caused, we at least can take some lessons from it in the intentional design of a scholarly network. There existed a rich exchange of medical knowledge between Africans and indigenous Americans that bypassed Europe entirely, taking an entirely different sort of route through early modern social networks.

If we take the present day, we see certain affordances of social networks similarly used to subvert or reconfigure power structures, as with the many revolutions in North Africa and the Middle East, or the current activist events taking place around police brutality and racism in the US. Similar tactics that piggy-back on network properties are used by governments to spread propaganda, ad agencies to spread viral videos, and so forth.

The question, then, is how we can intentionally design a community, using principles we learn from historical action, as well as modern network science, in order to subvert institutional structures in the manner raised by Cathy and David?

Certainly we also ought to take into account the research going into collaboration, teamwork, and group science. We’ve learned, for example, that teams with diverse backgrounds often come up with more creative solutions to tricky problems. We’ve learned that many small, agile groups often outperform large groups with the same amount of people, and that informal discussion outside the work-space contributes in interesting ways to productivity. Many great lessons can be found in Michael Nielsen’s book, Reinventing Discovery.

We can use these historical and lab-based examples to inform the design of social networks. HASTAC already work towards this goal through its scholars program, but there are more steps that may be taken, such as strategically seeking out scholars from underrepresented parts of the network.

So this covers covers the science, but what about the art?

Well, I spent the entire middle half of this talk discussing how visual rhetoric is linked to ontological metaphors of knowledge. The tree metaphor of knowledge, for example, was so strongly held that it fooled Descartes into breaking his claims of mind-body dualism.

So here is where the artists in the room can also fruitfully contribute to the same goal: by literally designing a better infrastructure. Visually. Illustrations can be remarkably powerful drivers of reconceptualization, and we have the opportunity here to affect changes in the academy more broadly.

One of the great gifts of the social web, at least when it’s designed well, is its ability to let nodes on the farthest limbs of the network to still wield remarkable influence over the whole structure. This is why viral videos, kickstarter projects, and cats playing pianos can become popular without “industry backing”. And the decisions we make in creating illustrations, in fostering online interactions, in designing social interfaces, can profoundly affect the way those interactions reinforce, subvert, or sidestep power structures.

So this is my call to the room: let’s revisit the discussion about designing the community we want to live in.

 

Thanks very much.

Digital History, Saturn’s Rings, and the Battle of Trafalgar

History and astronomy are a lot alike. When people claim history couldn’t possibly be scientific, because how can you do science without direct experimentation, astronomy should be used as an immediate counterexample.

Astronomers and historians both view their subjects from great distances; too far to send instruments for direct measurement and experimentation. Things have changed a bit in the last century for astronomy, of course, with the advent of machines sensitive enough to create earth-based astronomical experiments. We’ve also built ships to take us to the farthest reaches, for more direct observations.

Voyager 1 Spacecraft, on the cusp of interstellar space. [via]
Voyager 1 Spacecraft, on the cusp of interstellar space. [via]
It’s unlikely we’ll invent a time machine any time soon, though, so historians are still stuck looking at the past in the same way we looked at the stars for so many thousands of years: through a glass, darkly. Like astronomers, we face countless observational distortions, twisting the evidence that appears before us until we’re left with an echo of a shadow of the past. We recreate the past through narratives, combining what we know of human nature with the evidence we’ve gathered, eventually (hopefully) painting ever-clearer pictures of a time we could never touch with our fingers.

Some take our lack of direct access as a good excuse to shake away all trappings of “scientific” methods. This seems ill-advised. Retaining what we’ve learned over the past 50 years about how we construct the world we see is important, but it’s not the whole story, and it’s got enough parallels with 17th century astronomy that we might learn some lessons from that example.

Saturn’s Rings

In the summer 1610, Galileo observed Saturn through a telescope for the first time. He wrote with surprise that

Galileo's observation of Saturn through a telescope, 1610. [via]
Galileo’s Saturn. [via]

the star of Saturn is not a single star, but is a composite of three, which almost touch each other, never change or move relative to each other, and are arranged in a row along the zodiac, the middle one being three times larger than the two lateral ones…

This curious observation would take half a century to resolve into what we today see as Saturn’s rings. Galileo wrote that others, using inferior telescopes, would report seeing Saturn as oblong, rather than as three distinct spheres. Low and behold, within months, several observers reported an oblong Saturn.

Galileo's Saturn in 1616.
Galileo’s Saturn in 1616.

What shocked Galileo even more, however, was an observation two years later when the two smaller bodies disappeared entirely. They appeared consistently, with every observation, and then one day poof they’re gone. And when they eventually did come back, they looked remarkably odd.

Saturn sometimes looked as though it had “handles”, one connected to either side, but the nature of those handles were unknown to Galileo, as was the reason why sometimes it looked like Saturn had handles, sometimes moons, and sometimes nothing at all.

Saturn was just really damn weird. Take a look at these observations from Gassendi a few decades later:

Gassendi's Saturn [via]
Gassendi’s Saturn [via]
What the heck was going on? Many unsatisfying theories were put forward, but there was no real consensus.

Enter Christiaan Huygens, who in the 1650s was fascinated by the Saturn problem. He believed a better telescope was needed to figure out what was going on, and eventually got some help from his brother to build one.

The idea was successful. Within short order, Huygens developed the hypothesis that Saturn was encircled by a ring. This explanation, along with the various angles we would be viewing Saturn and its ring from Earth, accounted for the multitude of appearances Saturn could take. The figure below explains this:

Huygens' Saturn [via]
Huygens’ Saturn [via]
The explanation, of course, was not universally accepted. An opposing explanation by an anti-Copernican Jesuit contested that Saturn had six moons, the configuration of which accounted for the many odd appearances of the planet. Huygens countered that the only way such a hypothesis could be sustained would be with inferior telescopes.

While the exact details of the dispute are irrelevant, the proposed solution was very clever, and speaks to contemporary methods in digital history. The Accademia del Cimento devised an experiment that would, in a way, test the opposing hypotheses. They built two physical models of Saturn, one with a ring, and one with six satellites configured just-so.

The Model of Huygens' Saturn [via]
The Model of Huygens’ Saturn [via]
In 1660, the experimenters at the academy put the model of a ringed Saturn at the end of a 75-meter / 250-foot hallway. Four torches illuminated the model but were obscured from observers, so they wouldn’t be blinded by the torchlight.  Then they had observers view the model through various quality telescopes from the other end of the hallway. The observers were essentially taken from the street, so they wouldn’t have preconceived notions of what they were looking at.

Depending on the distance and quality of the telescope, observers reported seeing an oblong shape, three small spheres, and other observations that were consistent with what astronomers had seen. When seen through a glass, darkly, a ringed Saturn does indeed form the most unusual shapes.

In short, the Accademia del Cimento devised an experiment, not to test the physical world, but to test whether an underlying reality could appear completely different through the various distortions that come along with how we observe it. If Saturn had rings, would it look to us as though it had two small satellites? Yes.

This did not prove Huygens’ theory, but it did prove it to be a viable candidate given the observational instruments at the time. Within a short time, the ring theory became generally accepted.

The Battle of Trafalgar

So what’s Saturn’s ring have to do with the price of tea in China? What about digital history?

The importance is in the experiment and the model. You do not need direct access to phenomena, whether they be historical or astronomical, to build models, conduct experiments, or generally apply scientific-style methods to test, elaborate, or explore a theory.

In October 1805, Lord Nelson led the British navy to a staggering victory against the French and Spanish during the Napoleonic Wars. The win is attributed to Nelson’s unusual and clever battle tactics of dividing his forces in columns perpendicular to the single line of the enemy ships. Twenty-seven British ships defeated thirty-three Franco-Spanish ones. Nelson didn’t lose a single British ship lost, while the Franco-Spanish fleet lost twenty-two.

Horatio Nelson [via]
Horatio Nelson [via]
But let’s say the prevailing account is wrong. Let’s say, instead, due to the direction of the wind and the superior weaponry of the British navy, victory was inevitable: no brilliant naval tactician required.

This isn’t a question of counterfactual history, it’s simply a question of competing theories. But how can we support this new theory without venturing into counterfactual thinking, speculation? Obviously Nelson did lead the fleet, and obviously he did use novel tactics, and obviously a resounding victory ensued. These are indisputable historical facts.

It turns out we can use a similar trick to what the Accademia del Cimento devised in 1660: pretend as though things are different (Saturn has a ring; Nelson’s tactics did not win the battle), and see whether our observations would remain the same (Saturn looks like it is flanked by two smaller moons; the British still defeated the French and Spanish).

It turns out, further, that someone’s already done this. In 2003, two Italian physicists built a simulation of the Battle of Trafalgar, taking into account details of the ships, various strategies, wind direction, speed, and so forth. The simulation is a bit like a video game that runs itself: every ship has its own agency, with the ability to make decisions based on its environment, to attack and defend, and so forth.  It’s from a class of simulations called agent-based models.

When the authors directed the British ships to follow Lord Nelson’s strategy, of two columns, the fleet performed as expected: little loss of life on behalf of the British, major victory, and so forth. But when they ran the model without Nelson’s strategy, a combination of wind direction and superior British firepower still secured a British victory, even though the fleet was outnumbered.

…[it’s said] the English victory in Trafalgar is substantially due to the particular strategy adopted by Nelson, because a different plan would have led the outnumbered British fleet to lose for certain. On the contrary, our counterfactual simulations showed that English victory always occur unless the environmental variables (wind speed and direction) and the global strategies of the opposed factions are radically changed, which lead us to consider the British fleet victory substantially ineluctable.

Essentially, they tested assumptions of an alternative hypothesis, and found those assumptions would also lead to the observed results. A military historian might (and should) quibble with the details of their simplifying assumptions, but that’s all part of the process of improving our knowledge of the world. Experts disagree, replace simplistic assumptions with more informed ones, and then improve the model to see if the results still hold.

The Parable of the Polygons

This agent-based approach to testing theories about how society works is exemplified by the Schelling segregation model. This week the model shot to popularity through Vi Hart and Nicky Case’s Parable of the Polygons, a fabulous, interactive discussion of some potential causes of segregation. Go click on it, play through it, experience it. It’s worth it. I’ll wait.

Finished? Great! The model shows that, even if people only move homes if less than 1/3rd of their neighbors are the same color that they are, massive segregation will still occur. That doesn’t seem like too absurd a notion: everyone being happy with 2/3rds of their neighbors as another color, and 1/3rd as their own, should lead to happy, well-integrated communities, right?

Wrong, apparently. It turns out that people wanting 33% of their neighbors to be the same color as they are is sufficient to cause segregated communities. Take a look at the community created in Parable of the Polygons under those conditions:

Parable of the Polygons
Parable of the Polygons

This shows that very light assumptions of racism can still easily lead to divided communities. It’s not making claims about racism, or about society: what it’s doing is showing that this particular model, where people want a third of their neighbors to be like them, is sufficient to produce what we see in society today. Much like Saturn having rings is sufficient to produce the observation of two small adjacent satellites.

More careful work is needed, then, to decide whether the model is an accurate representation of what’s going on, but establishing that base, that the model is a plausible description of reality, is essential before moving forward.

Digital History

Digital history is a ripe field for this sort of research. Like astronomers, we cannot (yet?) directly access what came before us, but we can still devise experiments to help support our research, in finding plausible narratives and explanations of the past. The NEH Office of Digital Humanities has already started funding workshops and projects along these lines, although they are most often geared toward philosophers and literary historians.

The person doing the most thoughtful theoretical work at the intersection of digital history and agent-based modeling is likely Marten Düring, who is definitely someone to keep an eye on if you’re interested in this area. An early innovator and strong practitioner in this field is Shawn Graham, who actively blogs about related issues.  This technique, however, is far from the only one available to historians for devising experiments with the past. There’s a lot we can still learn from 17th century astronomers.

Understanding Special Relativity through History and Triangles (pt. 1)

We interrupt this usually-DH blog because I got in a discussion about Special Relativity with a friend, and promised it was easily understood using only the math we use for triangles. But I’m a historian, so I can’t leave a good description alone without some background.

If you just want to learn how relativity works, skip ahead to the next post, Relativity Made Simple [Note! I haven’t written it yet, this is a two-part post. Stay-tuned for the next section]; if you hate science and don’t want to know how the universe functions, but love history, read only this post. If you have a month of time to kill, just skip this post entirely and read through my 122-item relativity bibliography on Zotero. Everyone else, disregard this paragraph.

An Oddly Selective History of Relativity

This is not a history of how Einstein came up with his Theory of Special Relativity as laid out in Zur Elektrodynamik bewegter Körper in 1905. It’s filled with big words like aberration and electrodynamics, and equations with occult symbols. We don’t need to know that stuff. This is a history of how others understood relativity. Eventually, you’re going to understand relativity, but first I’m going to tell you how other people, much smarter than you, did not.

There’s an infamous (potentially mythical) story about how difficult it is to understand relativity: Arthur Eddington, a prominent astronomer, was asked whether it was true that only three people in the world understood relativity. After pausing for a moment, Eddington replies “I’m trying to think who the third person is!” This was about General Relativity, but it was also a joke: good scientists know relativity isn’t incredibly difficult to grasp, and even early on, lots of people could claim to understand it.

Good historians, however, know that’s not the whole story. It turns out a lot of people who thought they understood Einstein’s conceptions of relativity actually did not, including those who agreed with him. This, in part, is that story.

Relativity Before Einstein

Einstein’s special theory of relativity relied on two assumptions: (1) you can’t ever tell whether you’re standing still or moving at a constant velocity (or, in physics-speak, the laws of physics in any inertial reference frame are indistinguishable from one another), and (2) light always looks like it’s moving at the same speed (in physics-speak, the speed of light is always constant no matter the velocity of the emitting body nor that of the observer’s inertial reference frame). Let’s trace these concepts back.

Our story begins in the 14th century. William of Occam, famous for his razor, claimed motion was merely the location of a body and its successive positions over time; motion itself was in the mind. Because position was simply defined in terms of the bodies that surround it, this meant motion was relative. Occam’s student, Buridan, pushed that claim forward, saying “If anyone is moved in a ship and imagines that he is at rest, then, should he see another ship which is truly at rest, it will appear to him that the other ship is moved.”

Galileo's relativity [via]. The site where this comes from is a little crazy, but the figure is still useful, so here it is.
Galileo’s relativity [via]. The site where this comes from is a little crazy, but the figure is still useful, so here it is.
The story movies forward at irregular speed (much like the speed of this blog, and the pacing of this post). Within a century, scholars introduced the concepts of an infinite universe without any center, nor any other ‘absolute’ location. Copernicus cleverly latched onto this relativistic thinking by showing that the math works just as well, if not better, when the Earth orbits the Sun, rather than vice versa. Galileo claimed there was no way, on the basis of mechanical experiments, to tell whether you were standing still or moving at a uniform speed.

For his part, Descartes disagreed, but did say that the only way one could discuss movement was relative to other objects. Christian Huygens takes Descartes a step forward, showing that there are no ‘privileged’ motions or speeds (that is, there is no intrinsic meaning of a universal ‘at rest’ – only ‘at rest’ relative to other bodies). Isaac Newton knew that it was impossible to measure something’s absolute velocity (rather than velocity relative to an observer), but still, like Descartes, supported the idea that there was an absolute space and absolute velocity – we just couldn’t measure it.

Lets skip ahead some centuries. The year is 1893; the U.S. Supreme Court declared the tomato was a vegetable, Gandhi campaigned against segregation in South Africa, and the U.S. railroad industry bubble had just popped, forcing the government to bail out AIG for $85 billion. Or something. Also, by this point, most scientists thought light traveled in waves. Given that in order for something to travel in a wave, something has to be waving, scientists posited there was this luminiferous ether that pervaded the universe, allowing light to travel between stars and candles and those fish with the crazy headlights. It makes perfect sense. In order for sound waves to travel, they need air to travel through; in order for light waves to travel, they need the ether.

Ernst Mach, A philosopher read by many contemporaries (including Einstein), said that Newton and Descartes were wrong: absolute space and absolute motion are meaningless. It’s all relative, and only relative motion has any meaning. It is both physically impossible to measure the an objects “real” velocity, and also philosophically nonsensical. The ether, however, was useful. According to Mach and others, we could still measure something kind of like absolute position and velocity by measuring things in relationship to that all-pervasive ether. Presumably, the ether was just sitting still, doing whatever ether does, so we could use its stillness as a reference point and measure how fast things were going relative to it.

Well, in theory. Earth is hurtling through space, orbiting the sun at about 70,000 miles per hour, right? And it’s spinning too, at about a thousand miles an hour. But the ether is staying still. And light, supposedly, always travels at the same speed through the ether no matter what. So in theory, light should look like it’s moving a bit faster if we’re moving toward its source, relative to the ether, and a bit slower, if we’re moving away from it, relative to the ether. It’s just like if you’re in a train hurdling toward a baseball pitcher at 100 mph, and the pitcher throws a ball at you, also at 100 mph, in a futile attempt to stop the train. To you, the baseball will look like it’s going twice as fast, because you’re moving toward it.

The earth moving in the ether. [via]
The earth moving through the ether. [via]
It turns out measuring the speed of light in relation to the ether was really difficult. A bunch of very clever people made a bunch of very clever instruments which really should have measured the speed of earth moving through the ether, based on small observed differences of the speed of light going in different directions, but the experiments always showed light moving at the same speed. Scientists figured this must mean the earth was actually exerting a pull on the ether in its vicinity, dragging it along with it as the earth hurtled through space, explaining why light seemed to be constant in both directions when measured on earth. They devised even cleverer experiments that would account for such an ether drag, but even those seemed to come up blank. Their instruments, it was decided, simply were not yet fine-tuned enough to measure such small variations in the speed of light.

Not so fast! shouted Lorentz, except he shouted it in Dutch. Lorentz used the new electromagnetic theory to suggest that the null results of the ether experiments were actually a result, not of the earth dragging the ether along behind it, but of physical objects compressing when they moved against the ether. The experiments weren’t showing any difference in the speed of light they sought because the measuring instruments themselves contracted to just the right length to perfectly offset the difference in the velocity of light, when measuring “into” the ether. The ether was literally squeezing the electrons in the meter stick together so it became a little shorter; short enough to inaccurately measure light’s speed. The set of equations used to describe this effect became known as Lorentz Transformations. One property of these transformations was that the physical contractions would, obviously, appear the same from any observer. No matter how fast you were going relative to your measuring device, if it were moving into the ether, you would see it contracting slightly to accommodate the measurement difference.

Not so fast! shouted Poincaré, except he shouted it in French. This property of transformations to always appear the same, relative to the ether, was actually a problem. Remember that 500 years of physics that said there is no way to mechanically determine your absolute speed or absolute location in space? Yeah, so did Poincaré. He said the only way you could measure velocity or location was matter-to-matter, not matter-to-ether, so the Lorentz transformations didn’t fly.

It’s worth taking a brief aside to talk about the underpinnings of the theories of both Lorentz and Poincaré. Their theories were based on experimental evidence, which is to say, they based their reasoning on contraction on apparent experimental evidence of said contraction, and they based their theories of relativity off of experimental evidence of motion being relative.

Einstein and Relativity

When Einstein hit the scene in 1905, he approached relativity a bit differently. Instead of trying to fit the apparent contraction of objects from the ether drift experiment to a particular theory, Einstein began with the assumption that light always appeared to move at the same rate, regardless of the relative velocity of the observer. The other assumption he began with was that there was no privileged frame of reference; no absolute space or velocity, only the movement of matter relative to other matter. I’ll work out the math later, but, unsurprisingly, it turned out that working out these assumptions led to exactly the same transformation equations as Lorentz came up with experimentally.

The math was the same. The difference was in the interpretation of the math. Einstein’s theory required no ether, but what’s more, it did not require any physical explanations at all. Because Einstein’s theory of special relativity rested on two postulates about measurement, the theory’s entire implications rested in its ability to affect how we measure or observe the universe. Thus, the interpretation of objects “contracting,” under Einstein’s theory, was that they were not contracting at all. Instead, objects merely appear as though they contract relative to the movement of the observer. Another result of these transformation equations is that, from the perspective of the observer, time appears to move slower or faster depending on the relative speed of what is being observed. Lorentz’s theory predicted the same time dilation effects, but he just chalked it up to a weird result of the math that didn’t actually manifest itself. In Einstein’s theory, however, weird temporal stretching effects were Actually What Was Going On.

To reiterate: the math of Lorentz, Einstein, and Poincaré were (at least at this early stage) essentially equivalent. The result was that no experimental result could favor one theory over another. The observational predictions between each theory were exactly the same.

Relativity’s Supporters in America

I’m focusing on America here because it’s rarely focused on in the historiography, and it’s about time someone did. If I were being scholarly and citing my sources, this might actually be a novel contribution to historiography. Oh well, BLOG! All my primary sources are in that Zotero library I linked to earlier.

In 1910, Daniel Comstock wrote a popular account of the relativity of Lorentz and Einstein, to some extent conflating the two. He suggested that if Einstein’s postulates could be experimentally verified, his special theory of relativity would be true. “If either of these postulates be proved false in the future, then the structure erected can not be true in is present form. The question is, therefore, an experimental one.” Comstock’s statement betrays a misunderstanding of Einstein’s theory, though, because, at the time of that writing, there was no experimental difference between the two theories.

Gilbert Lewis and Richard Tolman presented a paper at the 1908 American Physical Society in New York, where they describe themselves as fully behind Einstein over Lorentz. Oddly, they consider Einstein’s theory to be correct, as opposed to Lorentz’s, because his postulates were “established on a pretty firm basis of experimental fact.” Which, to reiterate, couldn’t possibly have been a difference between Lorentz and Einstein. Even more oddly still, they presented the theory not as one of physics or of measurement, but of psychology (a bit like 14th century Oresme). The two went on to separately write a few articles which supposedly experimentally confirmed the postulates of special relativity.

In fact, the few Americans who did seem to engage with the actual differences between Lorentz and Einstein did so primarily in critique. Louis More, a well-respected physicist from Cincinnati, labeled the difference as metaphysical and primarily useless. This American critique was fairly standard.

At the 1909 America Physical Society meeting in Boston, one physicist (Harold Wilson) claimed his experiments showed the difference between Einstein and Lorentz. One of the few American truly theoretical physicists, W.S. Franklin, was in attendance, and the lectures he saw inspired him to write a popular account of relativity in 1911; in it, he found no theoretical difference between Lorentz and Einstein. He tended to side theoretically with Einstein, but assumed Lorentz’s theory implied the same space and time dilation effects, which they did not.

Even this series of misunderstandings should be taken as shining examples in the context of an American approach to theoretical physics that was largely antagonistic, at times decrying theoretical differences entirely. At a symposium on Ether Theories at the 1911 APS, the presidential address by William Magie was largely about the uselessness of relativity because, according to him, physics should be a functional activity based in utility and experimentation. Joining Magie’s “side” in the debate were Michelson, Morley, and Arthur Gordon Webster, the co-founder of the America Physical Society. Of those at the meeting supporting relativity, Lewis was still convinced Einstein differed experimentally from Lorentz, and Franklin and Comstock each felt there was no substantive difference between the two. In 1912, Indiana University’s R.D. Carmichael stated Einstein’s postulates were “a direct generalization from experiment.” In short, the American’s were really focused on experiment.

Of Einstein’s theory, Louis More wrote in 1912:

Professor Einstein’s theory of Relativity [… is] proclaimed somewhat noisily to be the greatest revolution in scientific method since the time of Newton. That [it is] revolutionary there can be no doubt, in so far as [it] substitutes mathematical symbols as the basis of science and denies that any concrete experience underlies these symbols, thus replacing an objective by a subjective universe. The question remains whether this is a step forward or backward […] if there is here any revolution in thought, it is in reality a return to the scholastic methods of the Middle Ages.

More goes on to say how the “Anglo-Saxons” demand practical results, not the unfathomable theories of “the German mind.” Really, that quote about sums it up. By this point, the only Americans who even talked about relativity were the ones who trained in Germany.

I’ll end here, where most histories of the reception of relativity begin: the first Solvay Conference. It’s where this beautiful picture was taken.

First Solvay Conference. [via]
First Solvay Conference. [via]
To sum up: in the seven year’s following Einstein’s publication, the only Americans who agreed with Einstein were ones who didn’t quite understand him. You, however, will understand it much better, if you only read the next post [coming this week!].

Do historians need scientists?

[edit: I’m realizing I didn’t make it clear in this post that I’m aware many historians consider themselves scientists, and that there’s plenty of scientific historical archaeology and anthropology. That’s exactly what I’m advocating there be more of, and more varied.]

Short Answer: Yes.

Less Snarky Answer: Historians need to be flexible to fresh methods, fresh perspectives, and fresh blood. Maybe not that last one, I guess, as it might invite vampires.Okay, I suppose this answer wasn’t actually less snarky.

Long Answer

The long answer is that historians don’t necessarily need scientists, but that we do need fresh scientific methods. Perhaps as an accident of our association with the ill-defined “humanities”, or as a result of our being placed in an entirely different culture (see: C.P. Snow), most historians seem fairly content with methods rooted in thinking about text and other archival evidence. This isn’t true of all historians, of course – there are economic historians who use statistics, historians of science who recreate old scientific experiments, classical historians who augment their research with archaeological findings, archival historians who use advanced ink analysis,  and so forth. But it wouldn’t be stretching the truth to say that, for the most part, historiography is the practice of thinking cleverly about words to make more words.

I’ll argue here that our reliance on traditional methods (or maybe more accurately, our odd habit of rarely discussing method) is crippling historiography, and is making it increasingly likely that the most interesting and innovative historical work will come from non-historians. Sometimes these studies are ill-informed, especially when the authors decide not to collaborate with historians who know the subject, but to claim that a few ignorant claims about history negate the impact of these new insights is an exercise in pedantry.

In defending the humanities, we like to say that scientists and technologists with liberal arts backgrounds are more well-rounded, better citizens of the world, more able to contextualize their work. Non-humanists benefit from a liberal arts education in pretty much all the ways that are impossible to quantify (and thus, extremely difficult to defend against budget cuts). We argue this in the interest of rounding a person’s knowledge, to make them aware of their past, of their place in a society with staggering power imbalances and systemic biases.

Humanities departments should take a page from their own books. Sure, a few general ed requirements force some basic science and math… but I got an undergraduate history degree in a nice university, and I’m well aware how little STEM I actually needed to get through it. Our departments are just as guilty of narrowness as those of our STEM colleagues, and often because of it, we rely on applied mathematicians, statistical physicists, chemists, or computer scientists to do our innovative work for (or sometimes, thankfully, with) us.

Of course, there’s still lots of innovative work to be done from a textual perspective. I’m not downplaying that. Not everyone needs to use crazy physics/chemistry/computer science/etc. methods. But there’s a lot of low hanging fruit at the intersection of historiography and the natural sciences, and we’re not doing a great job of plucking it.

The story below is illustrative.

Gutenberg

Last night, Blaise Agüera y Arcas presented his research on Gutenberg to a packed house at our rare books library. He’s responsible for a lot of the cool things that have come out of Microsoft in the last few years, and just got a job at Google, where presumably he will continue to make cool things. Blaise has degrees in physics and applied mathematics. And, a decade ago, Blaise and historian/librarian Paul Needham sent ripples through the History of the Book community by showing that Gutenberg’s press did not work at all the way people expected.

It was generally assumed that Gutenberg employed a method called punchcutting in order to create a standard font. A letter carved into a metal rod (a “punch”) would be driven into a softer metal (a “matrix”) in order to create a mold. The mold would be filled with liquid metal which hardened to form a small block of a single letter (a “type”), which would then be loaded onto the press next to other letters, inked, and then impressed onto a page. Because the mold was metal, many duplicate “types” could be made of the same letter, thus allowing many uses of the same letter to appear identical on a single pressed page.

Punch matrix system. [via]
Punch matrix system. [via]
Type to be pressed. [via]
Type to be pressed. [via]
This process is what allowed all the duplicate letters to appear identical in Gutenberg’s published books. Except, of course, careful historians of early print noticed that letters weren’t, in fact, identical. In the 1980s, Paul Needham and a colleague attempted to produce an inventory of all the different versions of letters Gutenberg used, but they stopped after frequently finding 10 or more obviously distinct versions of the same letter.

Needham's inventory of Gutenberg type. [via]
Needham’s inventory of Gutenberg type. [via]
This was perplexing, but the subject was bracketed away for a while, until Blaise Agüera y Arcas came to Princeton and decided to work with Needham on the problem. Using extremely high-resolution imagining techniques, Blaise noted that there were in fact hundreds of versions of every letter. Not only that, there were actually variations and regularities in the smaller elements that made up letters. For example, an “n” was formed by two adjacent vertical lines, but occasionally the two vertical lines seem to have flipped places entirely. The extremely basic letter “i” itself had many variations, but within those variations, many odd self-similarities.

Variations in the letter "i" in Gutenberg's type. [via]
Variations in the letter “i” in Gutenberg’s type. [via]
Historians had, until this analysis, assumed most letter variations were due to wear of the type blocks. This analysis blew that hypothesis out of the water. These “i”s were clearly not all made in the same mold; but then, how had they been made? To answer this, they looked even closer at the individual letters.

 

Close up of Gutenberg letters, with light shining through page. [via]
Close up of Gutenberg letters, with light shining through page. [via]
It’s difficult to see at first glance, but they found something a bit surprising. The letters appeared to be formed of overlapping smaller parts: a vertical line, a diagonal box, and so forth. The below figure shows a good example of this. The glyphs on the bottom have have a stem dipping below the bottom horizontal line, while the glyphs at the top do not.

Abbreviation of 'per'. [via]
Abbreviation of ‘per’. [via]
The conclusion Needham and Agüera y Arcas drew, eventually, was that the punchcutting method must not have been used for Gutenberg’s early material. Instead, a set of carved “strokes” were pushed into hard sand or soft clay, configured such that the strokes would align to form various letters, not unlike the formation of cuneiform. This mold would then be used to cast letters, creating the blocks we recognize from movable type. The catch is that this soft clay could only cast letters a few times before it became unusable and would need to be recreated. As Gutenberg needed multiple instances of individual letters per page, many of those letters would be cast from slightly different soft molds.

Low-Hanging Fruit

At the end of his talk, Blaise made an offhand comment: how is it that historians/bibliographers/librarians have been looking at these Gutenbergs for so long, discussing the triumph of their identical characters, and not noticed that the characters are anything but uniform? Or, of those who had noticed it, why hadn’t they raised any red flags?

The insights they produced weren’t staggering feats of technology. He used a nice camera, a light shining through the pages of an old manuscript, and a few simple image recognition and clustering algorithms. The clustering part could even have been done by hand, and actually had been, by Paul Needham. And yes, it’s true, everything is obvious in hindsight, but there were a lot of eyes on these bibles, and odds are if some of them had been historians who were trained in these techniques, this insight could have come sooner. Every year students do final projects and theses and dissertations, but what percent of those use techniques from outside historiography?

In short, there’s a lot of very basic assumptions we make about the past that could probably be updated significantly if we had the right skillset, or knew how to collaborate with those who did. I think people like William Newman, who performs Newton’s alchemical experiments, is on the right track. As is Shawn Graham, who reanimates the trade networks of ancient Rome using agent-based simulations, or Devon Elliott, who creates computational and physical models of objects from the history of stage magic. Elliott’s models have shown that certain magic tricks couldn’t possibly have worked as they were described to.

The challenge is how to encourage this willingness to reach outside traditional historiographic methods to learn about the past. Changing curricula to be more flexible is one way, but that is a slow and institutionally difficult process. Perhaps faculty could assign group projects to students taking their gen-ed history courses, encouraging disciplinary mixes and non-traditional methods. It’s an open question, and not an easy one, but it’s one we need to tackle.

Bridging Token and Type

There’s an oft-spoken and somewhat strawman tale of how the digital humanities is bridging C.P. Snow’s “Two Culture” divide, between the sciences and the humanities. This story is sometimes true (it’s fun putting together Ocean’s Eleven-esque teams comprising every discipline needed to get the job done) and sometimes false (plenty of people on either side still view the other with skepticism), but as a historian of science, I don’t find the divide all that interesting. As Snow’s title suggests, this divide is first and foremost cultural. There’s another overlapping divide, a bit more epistemological, methodological, and ontological, which I’ll explore here. It’s the nomothetic(type)/idiographic(token) divide, and I’ll argue here that not only are its barriers falling, but also that the distinction itself is becoming less relevant.

Nomothetic (Greek for “establishing general laws”-ish) and Idiographic (Greek for “pertaining to the individual thing”-ish) approaches to knowledge have often split the sciences and the humanities. I’ll offload the hard work onto Wikipedia:

Nomothetic is based on what Kant described as a tendency to generalize, and is typical for the natural sciences. It describes the effort to derive laws that explain objective phenomena in general.

Idiographic is based on what Kant described as a tendency to specify, and is typical for the humanities. It describes the effort to understand the meaning of contingent, unique, and often subjective phenomena.

These words are long and annoying to keep retyping, and so in the longstanding humanistic tradition of using new words for words which already exist, henceforth I shall refer to nomothetic as type and idiographic as token. 1 I use these because a lot of my digital humanities readers will be familiar with their use in text mining. If you counted the number of unique words in a text, you’d be be counting the number of types. If you counted the number of total words in a text, you’d be counting the number of tokens, because each token (word) is an individual instance of a type. You can think of a type as the platonic ideal of the word (notice the word typical?), floating out there in the ether, and every time it’s actually used, it’s one specific token of that general type.

The Token/Type Distinction
The Token/Type Distinction

Usually the natural and social sciences look for general principles or causal laws, of which the phenomena they observe are specific instances. A social scientist might note that every time a student buys a $500 textbook, they actively seek a publisher to punch, but when they purchase $20 textbooks, no such punching occurs. This leads to the discovery of a new law linking student violence with textbook prices. It’s worth noting that these laws can and often are nuanced and carefully crafted, with an awareness that they are neither wholly deterministic nor ironclad.

[via]
[via]
The humanities (or at least history, which I’m more familiar with) are more interested in what happened than in what tends to happen. Without a doubt there are general theories involved, just as in the social sciences there are specific instances, but the intent is most-often to flesh out details and create a particular internally consistent narrative. They look for tokens where the social scientists look for types. Another way to look at it is that the humanist wants to know what makes a thing unique, and the social scientist wants to know what makes a thing comparable.

It’s been noted these are fundamentally different goals. Indeed, how can you in the same research articulate the subjective contingency of an event while simultaneously using it to formulate some general law, applicable in all such cases? Rather than answer that question, it’s worth taking time to survey some recent research.

A recent digital humanities panel at MLA elicited responses by Ted Underwood and Haun Saussy, of which this post is in part itself a response. One of the papers at the panel, by Long and So, explored the extent to which haiku-esque poetry preceded what is commonly considered the beginning of haiku in America by about 20 years. They do this by teaching the computer the form of the haiku, and having it algorithmically explore earlier poetry looking for similarities. Saussy comments on this work:

[…] macroanalysis leads us to reconceive one of our founding distinctions, that between the individual work and the generality to which it belongs, the nation, context, period or movement. We differentiate ourselves from our social-science colleagues in that we are primarily interested in individual cases, not general trends. But given enough data, the individual appears as a correlation among multiple generalities.

One of the significant difficulties faced by digital humanists, and a driving force behind critics like Johanna Drucker, is the fundamental opposition between the traditional humanistic value of stressing subjectivity, uniqueness, and contingency, and the formal computational necessity of filling a database with hard decisions. A database, after all, requires you to make a series of binary choices in well-defined categories: is it or isn’t it an example of haiku? Is the author a man or a woman? Is there an author or isn’t there an author?

Underwood addresses this difficulty in his response:

Though we aspire to subtlety, in practice it’s hard to move from individual instances to groups without constructing something like the sovereign in the frontispiece for Hobbes’ Leviathan – a homogenous collection of instances composing a giant body with clear edges.

But he goes on to suggest that the initial constraint of the digital media may not be as difficult to overcome as it appears. Computers may even offer us a way to move beyond the categories we humanists use, like genre or period.

Aren’t computers all about “binary logic”? If I tell my computer that this poem both is and is not a haiku, won’t it probably start to sputter and emit smoke?

Well, maybe not. And actually I think this is a point that should be obvious but just happens to fall in a cultural blind spot right now. The whole point of quantification is to get beyond binary categories — to grapple with questions of degree that aren’t well-represented as yes-or-no questions. Classification algorithms, for instance, are actually very good at shades of gray; they can express predictions as degrees of probability and assign the same text different degrees of membership in as many overlapping categories as you like.

Here we begin to see how the questions asked of digital humanists (on the one side; computational social scientists are tackling these same problems) are forcing us to reconsider the divide between the general and the specific, as well as the meanings of categories and typologies we have traditionally taken for granted. However, this does not yet cut across the token/type divide: this has gotten us to the macro scale, but it does not address general principles or laws that might govern specific instances. Historical laws are a murky subject, prone to inducing fits of anti-deterministic rage. Complex Systems Science and the lessons we learn from Agent-Based Modeling, I think, offer us a way past that dilemma, but more on that later.

For now, let’s talk about influence. Or diffusion. Or intertextuality. 2 Matthew Jockers has been exploring these concepts, most recently in his book Macroanalysis. The undercurrent of his research (I think I’ve heard him call it his “dangerous idea”) is a thread of almost-determinism. It is the simple idea that an author’s environment influences her writing in profound and easy to measure ways. On its surface it seems fairly innocuous, but it’s tied into a decades-long argument about the role of choice, subjectivity, creativity, contingency, and determinism. One word that people have used to get around the debate is affordances, and it’s as good a word as any to invoke here. What Jockers has found is a set of environmental conditions which afford certain writing styles and subject matters to an author. It’s not that authors are predetermined to write certain things at certain times, but that a series of factors combine to make the conditions ripe for certain writing styles, genres, etc., and not for others. The history of science analog would be the idea that, had Einstein never existed, relativity and quantum physics would still have come about; perhaps not as quickly, and perhaps not from the same person or in the same form, but they were ideas whose time had come. The environment was primed for their eventual existence. 3

An example of shape affording certain actions by constraining possibilities and influencing people. [via]
An example of shape affording certain actions by constraining possibilities and influencing people. [via]
It is here we see the digital humanities battling with the token/type distinction, and finding that distinction less relevant to its self-identification. It is no longer a question of whether one can impose or generalize laws on specific instances, because the axes of interest have changed. More and more, especially under the influence of new macroanalytic methodologies, we find that the specific and the general contextualize and augment each other.

The computational social sciences are converging on a similar shift. Jon Kleinberg likes to compare some old work by Stanley Milgram 4, where he had people draw maps of cities from memory, with digital city reconstruction projects which attempt to bridge the subjective and objective experiences of cities. The result in both cases is an attempt at something new: not quite objective, not quite subjective, and not quite intersubjective. It is a representation of collective individual experiences which in its whole has meaning, but also can be used to contextualize the specific. That these types of observations can often lead to shockingly accurate predictive “laws” isn’t really the point; they’re accidental results of an attempt to understand unique and contingent experiences at a grand scale. 5

Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow unsure. [via Eric Fischer]
Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow are uncertain. [via Eric Fischer]
It is no surprise that the token/type divide is woven into the subjective/objective divide. However, as Daston and Galison have pointed out, objectivity is not an ahistorical category. 6 It has a history, is only positively defined in relation to subjectivity, and neither were particularly useful concepts before the 19th century.

I would argue, as well, that the nomothetic and idiographic divide is one which is outliving its historical usefulness. Work from both the digital humanities and the computational social sciences is converging to a point where the objective and the subjective can peaceably coexist, where contingent experiences can be placed alongside general predictive principles without any cognitive dissonance, under a framework that allows both deterministic and creative elements. It is not that purely nomothetic or purely idiographic research will no longer exist, but that they no longer represent a binary category which can usefully differentiate research agendas. We still have Snow’s primary cultural distinctions, of course, and a bevy of disciplinary differences, but it will be interesting to see where this shift in axes takes us.

Notes:

  1. I am not the first to do this. Aviezer Tucker (2012) has a great chapter in The Oxford Handbook of Philosophy of Social Science, “Sciences of Historical Tokens and Theoretical Types: History and the Social Sciences” which introduces and historicizes the vocabulary nicely.
  2. Underwood’s post raises these points, as well.
  3. This has sometimes been referred to as environmental possibilism.
  4. Milgram, Stanley. 1976. “Pyschological Maps of Paris.” In Environmental Psychology: People and Their Physical Settings, edited by Proshansky, Ittelson, and Rivlin, 104–124. New York.

    ———. 1982. “Cities as Social Representations.” In Social Representations, edited by R. Farr and S. Moscovici, 289–309.

  5. If you’re interested in more thoughts on this subject specifically, I wrote a bit about it in relation to single-authorship in the humanities here
  6. Daston, Lorraine, and Peter Galison. 2007. Objectivity. New York, NY: Zone Books.

From Trees to Webs: Uprooting Knowledge through Visualization

[update: here are some of the pretty pictures I will be showing off in The Hague]

The blog’s been quiet lately; my attention has been occupied by various journal submissions and a new book in the works, but I figured my readers would be interested in one of those forthcoming publications. This is an article [preprint] I’m presenting at the Universal Decimal Classification Seminar in The Hague this October, on the history of how we’ve illustrated the interconnections of knowledge and scholarly domains. It’s basically two stories: one of how we shifted from understanding the world hierarchically to understanding it as a flat web of interconnected parts, and the other of how the thing itself and knowledge of that thing became separated.

Porphyrian Tree: tree of Aristotle's categories from the 6th century. [via]
Porphyrian Tree: tree of Aristotle’s categories originally dating from the 6th century. [via some random website about trees]
A few caveats worth noting: first, because I didn’t want to deal with the copyright issues, there are no actual illustrations in the paper. For the presentation, I’m going to compile a powerpoint with all the necessary attributions and post it alongside this paper so you can all see the relevant pretty pictures. For your viewing pleasure, though, I’ve included some of the illustrations in this blog post.

An interpretation of the classification of knowledge from Hobbes' Leviathan. [via e-ducation]
An interpretation of the classification of knowledge from Hobbes’ Leviathan. [via e-ducation]
Second, because the this is a presentation directed at information scientists, the paper is organized linearly and with a sense of inevitability; or, as my fellow historians would say, it’s very whiggish. I regret not having the space to explore the nuances of the historical narrative, but it would distract from the point and context of this presentation. I plan on writing a more thorough article to submit to a history journal at a later date, hopefully fitting more squarely in the historiographic rhetorical tradition.

H.G. Wells' idea of how students should be taught. [via H.G. Wells, 1938. World Brain. Doubleday, Doran & Co., Inc]
H.G. Wells’ idea of how students should be taught. [via H.G. Wells, 1938. World Brain. Doubleday, Doran & Co., Inc]
In the meantime, if you’re interested in reading the pre-print draft, here it is! All comments are welcome, as like I said, I’d like to make this into a fuller scholarly article beyond the published conference proceedings. I was excited to put this up now, but I’ll probably have a new version with full citation information within the week, if you’re looking to enter this into Zotero/Mendeley/etc. Also, hey! I think this is the first post on the Irregular that has absolutely nothing to do with data analysis.

Recent map of science by Kevin Boyack, Dick Klavans, W. Bradford Paley, and Katy Börner. [via SEED magazine]
Recent map of science by Kevin Boyack, Dick Klavans, W. Bradford Paley, and Katy Börner. [via SEED magazine]