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.
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.
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 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.
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.
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.
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.
Below is some crazy, uninformed ramblings about the least-complex possible way to trick someone into thinking a computer is a human, for the purpose of history research. I’d love some genuine AI/Machine Intelligence researchers to point me to the actual discussions on the subject. These aren’t original thoughts; they spring from countless sci-fi novels and AI research from the ’70s-’90s. Humanists beware: this is super sci-fi speculative, but maybe an interesting thought experiment.
If someone’s chatting with a computer, but doesn’t realize her conversation partner isn’t human, that computer passes the Turing Test. Unrelatedly, if a robot or piece of art is just close enough to reality to be creepy, but not close enough to be convincingly real, it lies in the Uncanny Valley. I argue there is a useful concept in the simplest possible computer which is still convincingly human, and that computer will be at the Turing Point. 1
Forgive my twisting Turing Tests and Uncanny Valleys away from their normal use, for the sake of outlining the Turing Point concept:
A human simulacrum is a simulation of a human, or some aspect of a human, in some medium, which is designed to be as-close-as-possible to that which is being modeled, within the scope of that medium.
A Turing Test winner is any human simulacrum which humans consistently mistake for the real thing.
An occupant of the Uncanny Valley is any human simulacrum which humans consistently doubt as representing a “real” human.
Between the Uncanny Valley and Turing Test winners lies the Turing Point, occupied by the least-sophisticated human simulacrum that can still consistently pass as human in a given medium. The Turing Point is a hyperplane in a hypercube, such that there are many points of entry for the simulacrum to “phase-transition” from uncanny to convincing.
Extending the Turing Test
The classic Turing Test scenario is a text-only chatbot which must, in free conversation, be convincing enough for a human to think it is speaking with another human. A piece of software named Eugene Goostman sort-of passed this test in 2014, convincing a third of judges it was a 13-year-old Ukrainian boy.
There are many possible modes in which a computer can act convincingly human. It is easier to make a convincing simulacrum of a 13-year-old non-native English speaker who is confined to text messages than to make a convincing college professor, for example. Thus the former has a lower Turing Point than the latter.
Playing with the constraints of the medium will also affect the Turing Point threshold. The Turing Point for a flesh-covered robot is incredibly difficult to surpass, since so many little details (movement, design, voice quality, etc.) may place it into the Uncanny Valley. A piece of software posing as a Twitter user, however, would have a significantly easier time convincing fellow users it is human.
The Turing Point, then, is flexible to the medium in which the simulacrum intends to deceive, and the sort of human it simulates.
From Type to Token
Convincing the world a simulacrum is any old human is different than convincing the world it is some specific human. This is the token/type distinction; convincingly simulating a specific person (token) is much more difficult than convincingly simulating any old person (type).
Simulations of specific people are all over the place, even if they don’t intend to deceive. Several Twitter-bots exist as simulacra of Donald Trump, reading his tweets and creating new ones in a similar style. Perhaps imitating Poe’s Law, certain people’s styles, or certain types of media (e.g. Twitter), may provide such a low Turing Point that it is genuinely difficult to distinguish humans from machines.
Put differently, the way some Turing Tests may be designed, humans could easily lose.
It’ll be useful to make up and define two terms here. I imagine the concepts already exist, but couldn’t find them, so please comment if they do so I can use less stupid words:
A type-bot is a machine designed to be represent something at the type-level. For example, a bot that can be mistaken for some random human, but not some specific human.
A token-bot is a machine designed to represent something at the token-level. For example, a bot that can be mistaken for Donald Trump.
This all got me thinking, if we reach the Turing Point for some social media personalities (that is, it is difficult to distinguish between their social media presence, and a simulacrum of it), what’s to say we can’t reach it for an entire social media ecosystem? Can we take a snapshot of Twitter and project it several seconds/minutes/hours/days into the future, a bit like a meteorological model?
A few questions and obvious problems:
Much of Twitter’s dynamics are dependent upon exogenous forces: memes from other media, real world events, etc. Thus, no projection of Twitter alone would ever look like the real thing. One can, however, potentially use such a simulation to predict how certain types of events might affect the system.
This is way overkill, and impossibly computationally complex at this scale. You can simulate the dynamics of Twitter without simulating every individual user, because people on average act pretty systematically. That said, for the humanities-inclined, we may gain more insight from the ground-level of the system (individual agents) than macroscopic properties.
This is key. Would a set of plausibly-duplicate Twitter personalities on aggregate create a dynamic system that matches Twitter as an aggregate system? That is, just because the algorithms pass the Turing Test, because humans believe them to be humans, does that necessarily imply the algorithms have enough fidelity to accurately recreate the dynamics of a large scale social network? Or will small unnoticeable differences between the simulacrum and the original accrue atop each other, such that in aggregate they no longer act like a real social network?
The last point is I think a theoretically and methodologically fertile one for people working in DH, AI, and Cognitive Science: whether reducing human-appreciable traits between machines and people is sufficient to simulate aggregate social behavior, or whether human-appreciability (i.e., Turing Test) is a strict enough criteria for making accurate predictions about societies.
These points aside, if we ever do manage to simulate specific people (even in a very limited scope) as token-bots based on the traces they leave, it opens up interesting pedagogical and research opportunities for historians. Scott Enderle tweeted a great metaphor for this:
@scott_bot In one of the thousand science fiction stories I’ll never write, “nachlass” is the name for an archived consciousness.
Imagine, as a student, being able to have a plausible discussion with Marie Curie, or sitting in an Enlightenment-era salon. 2 Or imagine, as a researcher (if individual Turing Point machines do aggregate well), being able to do well-grounded counterfactual history that works at the token level rather than at the type level.
Turing Point Simulations
Bringing this slightly back into the realm of the sane, the interesting thing here is the interplay between appreciability (a person’s ability to appreciate enough difference to notice something wrong with a simulacrum) and fidelity.
We can specifically design simulation conditions with incredibly low-threshold Turing Points, even for token-bots. That is to say, we can create a condition where the interactions are simple enough to make a bot that acts indistinguishably from the specific human it is simulating.
At the most extreme end, this is obviously pointless. If our system is one in which a person can only answer “yes” or “no” to pre-selected preference questions (“Do you like ice-cream?”), making a bot to simulate that person convincingly would be trivial.
Putting that aside (lest we get into questions of the Turing Point of a set of Turing Points), we can potentially design reasonably simplistic test scenarios that would allow for an easy-to-reach Turing Point while still being historiographically or sociologically useful. It’s sort of a minimization problem in topological optimizations. Such a goal would limit the burden of the simulation while maximizing the potential research benefit (but only if, as mentioned before, the difference between true fidelity and the ability to win a token-bot Turing Test is small enough to allow for generalization).
In short, the concept of a Turing Point can help us conceptualize and build token-simulacra that are useful for research or teaching. It helps us ask the question: what’s the least-complex-but-still-useful token-simulacra? It’s also kind-of maybe sort-of like Kolmogorov complexity for human appreciability of other humans: that is, the simplest possible representation of a human that is convincing to other humans.
I’ll end by saying, once again, I realize how insane this sounds, and how far-off. And also how much an interloper I am to this space, having never so much as designed a bot. Still, as Bill Hart-Davidson wrote,
@scott_bot I agree, and that scale is more clearly plausible than it all seemed years ago reading Mona Lisa Overdrive
the possibility seems more plausible than ever, even if not soon-to-come. I’m not even sure why I posted this on the Irregular, but it seemed like it’d be relevant enough to some regular readers’ interests to be worth spilling some ink.
The name itself is maybe too on-the-nose, being a pun for turning point and thus connected to the rhetoric of singularity, but ¯\_(ツ)_/¯ ↩
Yes yes I know, this is SecondLife all over again, but hopefully much more useful. ↩
One limitation of our study is we know very little about the content of conference presentations or the racial identities of authors, which means we can’t assess bias in those directions. John D. Martin III & Carolyn Runyon recently published preliminary results more thoroughly addressing race & gender in DH from a funding perspective, and focused on the content of grants:
By hand-categorizing 656 DH-oriented NEH grants from 2007-2016, totaling $225 million, Martin & Runyon found 110 projects whose focus involved gender or individuals of a certain gender, and 228 which focused on race/ethnicity or individuals identifiable with particular races/ethnicities.
Major findings include:
Twice as much money goes to studying men than to women.
On average, individual projects about women are better-funded.
The top three race/ethnicity categories by funding amount are White ($21 million), Asian ($7 million), and Black ($6.5 million).
White men are discussed as individuals, and women and non-white people are focused on as groups.
Their results fit well with what I and others have found, which is that DH propagates the same cultural bias found elsewhere within and outside academia.
A next step, vital to this project, is to find equivalent metrics for other disciplines and data sources. Until we get a good baseline, we won’t actually know if our interventions are improving the situation. It’s all well and good to say “things are bad”, but until we know the compared-to-what, we won’t have a reliable way of testing what works and what doesn’t.
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.
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.
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.
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.
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 cited. That’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.
This is all pretty hand-wavy. How do we know success is more important than quality in predicting success? Uh, basically because of Napster.
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.
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.
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.
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.
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 Philosophyhad 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”.
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.
Incidentally, that’s why the last advice you ever want to take is someone telling you how to succeed from their own experience.
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.
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.
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.
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.
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.
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.
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!
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.
Unless it’s really awful, but let’s avoid that discussion here. ↩
A combination of human error (my own), accruing chaos, the complexities of WordPress, and the awful-but-cheap hosting solution that is bluehost.com. As many noticed, the site’s been slowing down, interactive elements like my photo gallery stopped working, and by last week, pages would go dark for hours at a time. By this week, bluehost no longer allowed me ftp or cpanel access. So yesterday I took my business to ReclaimHosting.com, the hands-down best (and friendliest) hosting service for personal and small-scale academic websites.
I still haven’t figured out what finally did it in, but with so many moving parts, it seemed better to start fresh than repair the beast. I’m currently working on a jekyll static website; this new wordpress blog you’re reading now is an interim solution. However, I couldn’t just cut my losses and start over, since I’ve put a lot of my soul into the 100+ blog posts & pages I’ve written here since 2009.
More importantly, my site has been cited in dozens of articles, and appears on the syllabus of hundreds of courses, DH and otherwise. If I delete the content, I’m destroying part of the scholarly record, and potentially ruining the morning of professors who assign my blog posts as reading, only to find out at the last minute that it no longer exists.
Here lies the problem. Because I no longer had back-end access to my website, I could not download my content through the usual channels. Because of the peculiarities of my various WordPress customizations, not worth detailing here, I could not use a plugin to export my site and its contents.
Since I wanted the form of my site preserved for the scholarly record, the only solution I could come up with was to crawl my entire site, externally, and download static html versions of each page on scottbot.net as it used to exist.
This is where the double-meaning of fix, described above, comes into play. I wanted the site functioning again, not broken, but I also wanted to preserve the old website as it existed at the URLs everyone has already linked to. For example, a bunch of syllab(uses|i) link to http://www.scottbot.net/HIAL/?tag=networks-demystified to direct their students to my networks demystified posts. I wanted to make sure that URL would continue to point to the version of the site they intended to link to, while also migrating the old content into a new system that I’d be able to update more fluidly. Thankfully the old directory for the site, /HIAL/ (the site used to be called History Is ALab), made that easier: the new version of the irregular would reside on scottbot.net, and the archive would remain on scottbot.net/HIAL/.
This apparently isn’t trivial. The first step was to use wget (explained and taught by Ian Milligan on the Programming Historian) to download a static version of the entire original irregular. After fiddling with the wget parameters and redownloading my site a few times, I ended up with a mostly-complete mirror of all the old content. Then I uploaded the entire mirror to my new host in the /HIAL/ directory. Yay!
The .htaccess file sits on your server, quietly directing traffic to the various places it should go. In my case, I needed to use regular expressions (remember that thing Shawn, Ian, and I taught in The Historian’s Macroscope?) to redirect all traffic pointing to HIAL/?[anything] to HIAL/index.html@[anything]. An hour or so of learning how .htaccess worked resulted in:
which, after some false starts, seems to work. The old site is now fixed, as in constant; secured; unwavering, at scottbot.net/HIAL/. The new irregular, at scottbot.net, is now fixed, as in functional, dynamic. It will continue to evolve and change.
the scottbot irregular is dead. Long live the scottbot irregular!
Nickoal Eichmann (corresponding author), Jeana Jorgensen, Scott B. Weingart1
NOTE: This is a pre-peer reviewed draft submitted for publication in Feminist Debates in Digital Humanities, eds. Jacque Wernimont and Elizabeth Losh, University of Minnesota Press (2017). Comments are welcome, and a downloadable dataset / more figures are forthcoming. This chapter will be released alongside another on the history of DH conferences, co-authored by Weingart & Eichmann (forthcoming), which will go into further detail on technical aspects of this study, including the data collection & statistics. Many of the materials first appeared on this blog. To cite this preprint, use the figshare DOI: https://dx.doi.org/10.6084/m9.figshare.3120610.v1
Digital Humanities (DH) is said to have a light side and a dark side. Niceness, globality, openness, and inclusivity sit at one side of this binary caricature; commodification, neoliberalism, techno-utopianism, and white male privilege sit at the other. At times, the plurality of DH embodies both descriptions.
We hope a diverse and critical DH is a goal shared by all. While DH, like the humanities writ large, is not a monolith, steps may be taken to improve its public face and shared values through positively influencing its communities. The Alliance of Digital Humanities Organizations’ (ADHO’s) annual conference hosts perhaps the largest such community. As an umbrella organization of six international digital humanities constituent organizations, as well as 200 DH centers in a few dozen countries, ADHO and its conference ought to represent the geographic, disciplinary, and demographic diversity of those who identify as digital humanists.
The annual conference offers insight into how the world sees DH. While it may not represent the plurality of views held by self-described digital humanists, the conference likely influences the values of its constituents. If the conference glorifies Open Access, that value will be taken up by its regular attendees; if the conference fails to prioritize diversity, this too will be reinforced.
This chapter explores fifteen years of DH conferences, presenting a quantified look at the values implicitly embedded in the event. Women are consistently underrepresented, in spite of the fact that the most prominent figures at the conference are as likely women as men. The geographic representation of authors has become more diverse over time—though authors with non-English names are still significantly less likely to pass peer review. The topical landscape is heavily gendered, suggesting a masculine bias may be built into the value system of the conference itself. Without data on skin color or ethnicity, we are unable to address racial or related diversity and bias here.
There have been some improvements over time and, especially recently, a growing awareness of diversity-related issues. While many of the conference’s negative traits are simply reflections of larger entrenched academic biases, this is no comfort when self-reinforcing biases foster a culture of microaggression and white male privilege. Rather than using this study as an excuse to write off DH as just another biased community, we offer statistics, critiques, and suggestions as a vehicle to improve ADHO’s conference, and through it the rest of self-identified Digital Humanities.
Digital humanities (DH), we are told, exists under a “big tent”, with porous borders, little gatekeeping, and, heck, everyone’s just plain “nice”. Indeed, the term itself is not used definitionally, but merely as a “tactical convenience” to get stuff done without worrying so much about traditional disciplinary barriers. DH is “global”, “public”, and diversely populated. It will “save the humanities” from its crippling self-reflection (cf. this essay), while simultaneously saving the computational social sciences from their uncritical approaches to data. DH contains its own mirror: it is both humanities done digitally, and the digital as scrutinized humanistically. As opposed to the staid, backwards-looking humanities we are used to, the digital humanities “experiments”, “plays”, and even “embraces failure” on ideological grounds. In short, we are the hero Gotham needs.
Digital Humanities, we are told, is a narrowly-defined excuse to push a “neoliberal agenda”, a group of “bullies” more interested in forcing humanists to code than in speaking truth to power. It is devoid of cultural criticism, and because of the way DHers uncritically adopt tools and methods from the tech industry, they in fact often reinforce pre-existing power structures. DH is nothing less than an unintentionally rightist vehicle for techno-utopianism, drawing from the same font as MOOCs and complicit in their devaluing of education, diversity, and academic labor. It is equally complicit in furthering both the surveillance state and the surveillance economy, exemplified in its stunning lack of response to the Snowden leaks. As a progeny of the computer sciences, digital humanities has inherited the same lack of gender and racial diversity, and any attempt to remedy the situation is met with incredible resistance.
The truth, as it so often does, lies somewhere in the middle of these extreme caricatures. It’s easy to ascribe attributes to Digital Humanities synecdochically, painting the whole with the same brush as one of its constituent parts. One would be forgiven, for example, for coming away from the annual international ADHO Digital Humanities conference assuming DH were a parade of white men quantifying literary text. An attendee of HASTAC, on the other hand, might leave seeing DH as a diverse community focused on pedagogy, but lacking in primary research. Similar straw-snapshots may be drawn from specific journals, subcommunities, regions, or organizations.
But these synecdoches have power. Our public face sets the course of DH, via who it entices to engage with us, how it informs policy agendas and funding allocations, and who gets inspired to be the next generation of digital humanists. Especially important is the constituency and presentation of the annual Digital Humanities conference. Every year, several hundred students, librarians, staff, faculty, industry professionals, administrators and researchers converge for the conference, organized by the Alliance of Digital Humanities Organizations (ADHO). As an umbrella organization of six international digital humanities constituent organizations, as well as 200 DH centers in a few dozen countries, ADHO and its conference ought to represent the geographic, disciplinary, and demographic diversity of those who identify as digital humanists. And as DH is a community that prides itself on its activism and its social/public goals, if the annual DH conference does not celebrate this diversity, the DH community may suffer a crisis of identity (…okay, a bigger crisis of identity).
So what does the DH conference look like, to an outsider? Is it diverse? What topics are covered? Where is it held? Who is participating, who is attending, and where are they coming from? This essay offers incomplete answers to these questions for fifteen years of DH conferences (2000-2015), focusing particularly on DH2013 (Nebraska, USA), DH2014 (Lausanne, Switzerland), and DH2015 (Sydney, Australia). 2 We do so with a double-agenda: (1) to call out the biases and lack of diversity at ADHO conferences in the earnest hope it will help improve future years’ conferences, and (2) to show that simplistic, reductive quantitative methods can be applied critically, and need not feed into techno-utopic fantasies or an unwavering acceptance of proxies as a direct line to Truth. By “distant reading” DH and turning our “macroscopes” on ourselves, we offer a critique of our culture, and hopefully inspire fruitful discomfort in DH practitioners who apply often-dehumanizing tools to their subjects, but have not themselves fallen under the same distant gaze.
Among other findings, we observe a large gender gap for authorship that is not mirrored among those who simply attend the conference. We also show a heavily gendered topical landscape, which likely contributes to topical biases during peer review. Geographic diversity has improved over fifteen years, suggesting ADHO’s strategy to expand beyond the customary North American / European rotation was a success. That said, there continues to be a visible bias against non-English names in the peer review process. We could not get data on ethnicity, race, or skin color, but given our regional and name data, as well as personal experience, we suspect in this area, diversity remains quite low.
We do notice some improvement over time and, especially in the last few years, a growing awareness of our own diversity problems. The #whatifDH2016 3 hashtag, for example, was a reaction to an all-male series of speakers introducing DH2015 in Sydney. The hashtag caught on and made it to ADHO’s committee on conferences, who will use it in planning future events. Our remarks here are in the spirit of #whatifDH2016; rather than using this study as an excuse to defame digital humanities, we hope it becomes a vehicle to improve ADHO’s conference, and through it the rest of our community.
Social Justice and Equality in the Digital Humanities
Diversity in the Academy
In order to contextualize gender and ethnicity in the DH community, we must take into account developments throughout higher education. This is especially important since much of DH work is done in university and other Ivory Tower settings. Clear progress has been made from the times when all-male, all-white colleges were the norm, but there are still concerns about the marginalization of scholars who are not white, male, able-bodied, heterosexual, or native English-speakers. Many campuses now have diversity offices and have set diversity-related goals at both the faculty and student levels (for example, see the Ohio State University’s diversity objectives and strategies 2007-12). On the digital front, blogs such as Conditionally Accepted, Fight the Tower, University of Venus, and more all work to expose the normative biases in academia through activist dialogue.
From both a historical and contemporary lens, there is data supporting the clustering of women and other minority scholars in certain realms of academia, from specific fields and subjects to contingent positions. When it comes to gender, the phrase “feminization” has been applied both to academia in general and to specific fields. It contains two important connotations: that of an area in which women are in the majority, and the sense of a change over time, such that numbers of women participants are increasing in relation to men (Leathwood and Read 2008, 10). It can also signal a less quantitative shift in values, “whereby ‘feminine’ values, concerns, and practices are seen to be changing the culture of an organization, a field of practice or society as a whole” (ibid).
In terms of specific disciplines, the feminization of academia has taken a particular shape. Historian Lynn Hunt suggests the following propositions about feminization in the humanities and history specifically: the feminization of history parallels what is happening in the social sciences and humanities more generally; the feminization of the social sciences and humanities is likely accompanied by a decline in status and resources; and other identity categories, such as ethnic minority status and age/generation, also interact with feminization in ways that are still becoming coherent.
Feminization has clear consequences for the perception and assignation of value of a given field. Hunt writes: “There is a clear correlation between relative pay and the proportion of women in a field; those academic fields that have attracted a relatively high proportion of women pay less on average than those that have not attracted women in the same numbers.” Thus, as we examine the topics that tend to be clustered by gender in DH conference submissions, we must keep in mind the potential correlations of feminization and value, though it is beyond the scope of this paper to engage in chicken-or-egg debates about the causal relationship between misogyny and the devaluing of women’s labor and women’s topics.
There is no obvious ethnicity-based parallel to the concept of the feminization of academia; it wouldn’t be culturally intelligible to talk about the “people-of-colorization of academia”, or the “non-white-ization of academia.” At any rate, according to a U.S. Department of Education survey, in 2013 79% of all full-time faculty in degree-granting postsecondary institutions were white. The increase of non-white faculty from 2009 (19.2% of the whole) to 2013 (21.5%) is very small indeed.
Why does this matter? As Jeffrey Milem, Mitchell Chang, and Anthony Lising Antonio write in regard to faculty of color, “Having a diverse faculty ensures that students see people of color in roles of authority and as role models or mentors. Faculty of color are also more likely than other faculty to include content related to diversity in their curricula and to utilize active learning and student-centered teaching techniques…a coherent and sustained faculty diversity initiative must exist if there is to be any progress in diversifying the faculty” (25). By centering marginalized voices, scholarly institutions have the ability to send messages about who is worthy of inclusion.
Recent Criticisms of Diversity in DH
In terms of DH specifically, diversity within the community and conferences has been on the radar for several years, and has recently gained special attention, as digital humanists and other academics alike have called for critical and feminist engagement in diversity and a move away from what seems to be an exclusionary culture. In January 2011, THATCamp SoCal included a section called “Diversity in DH,” in which participants explored the lack of openness in DH and, in the end, produced a document, “Toward an Open Digital Humanities” that summarized their discussions. The “Overview” in this document mirrors the same conversation we have had for the last several years:
We recognize that a wide diversity of people is necessary to make digital humanities function. As such, digital humanities must take active strides to include all the areas of study that comprise the humanities and must strive to include participants of diverse age, generation, sex, skill, race, ethnicity, sexuality, gender, ability, nationality, culture, discipline, areas of interest. Without open participation and broad outreach, the digital humanities movement limits its capacity for critical engagement. (ibid)
This proclamation represents the critiques of the DH landscape in 2011, in which DH practitioners and participants were assumed to be privileged and white, that they excluded student-learners, and that they held myopic views of what constitutes DH. Most importantly for this chapter, THATCamp SoCal’s “Diversity in DH” section participants called for critical approaches and social justice of DH scholarship and participation, including “principles for feminist/non-exclusionary groundrules in each session (e.g., ‘step up/step back’) so that the loudest/most entitled people don’t fill all the quiet moments.” They also advocated defending the least-heard voices “so that the largest number of people can benefit…”
These voices certainly didn’t fall flat. However, since THATCamps are often comprised of geographically local DH microcommunities, they benefit from an inclusive environment but suffer as isolated events. As result, it seems that the larger, discipline-specific venues which have greater attendance and attraction continue to amplify privileged voices. Even so, 2011 continued to represent a year that called for critical engagement in diversity in DH, with an explicit “Big Tent” theme for DH2011 held in Stanford, California. Embracing the concept the “Big Tent” deliberately opened the doors and widened the spectrum of DH, at least in terms of methods and approaches. However, as Melissa Terras pointed out, DH was “still a very rich, very western academic field” (Terras, 2011), even with a few DH2011 presentations engaging specifically with topics of diversity in DH. 4
A focus on diversity-related issues has only grown in the interim. We’ve recently seen greater attention and criticism of DH exclusionary culture, for instance, at the 2015 Modern Language Association (MLA) annual convention, which included the roundtable discussion “Disrupting Digital Humanities.” It confronted the “gatekeeping impulse” in DH, and echoing THATCamp SoCal 2011, these panelists aimed to shut down hierarchical dialogues in DH, encourage non-traditional scholarship, amplify “marginalized voices,” advocate for DH novices, and generously support the work of peers. 5 The theme for DH2015 in Sydney, Australia was “Global Digital Humanities,” and between its successes and collective action arising from frustrations at its failures, the community seems poised to pay even greater attention to diversity. Other recent initiatives in this vein worth mention include #dhpoco, GO::DH, and Jacqueline Wernimont’s “Build a Better Panel,” 6 whose activist goals are helping diversify the community and raise awareness of areas where the community can improve.
While it would be fruitful to conduct a longitudinal historiographical analysis of diversity in DH, more recent criticisms illustrate a history of perceived exclusionary culture, which is why we hope to provide a data-driven approach to continue the conversation and call for feminist and critical engagement and intervention.
While DH as a whole has been critiqued for its lack of diversity and inclusion, how does the annual ADHO DH conference measure up? To explore this in a data-driven fashion, we have gathered publicly available annual ADHO conference programs and schedules from 2000-2015. From those conference materials, we have entered presentation and author information into a spreadsheet to analyze various trends over time, such as gender and geography as indicators of diversity. Particular information that we have collected includes: presentation title, keywords (if available), abstract and full-text (if available), presentation type, author name, author institutional affiliation and academic department (if available), and corresponding country of that affiliation at the time of the presentation(s). We normalized and hand-cleaned names, institutions, and departments, so that, to the best of our knowledge, each author entry represented a unique person and, accordingly, was assigned a unique ID. Next, we added gender information (m/f/other/unknown) to authors by a combination of hand-entry and automated inference. While this is problematic for many reasons, 7 since it does not allow for diversity in gender options and tracing gender changes over time, it does give us a useful preliminary lense to view gender diversity at DH conferences.
For 2013’s conference, ADHO instituted a series of changes aimed at improving inclusivity, diversity, and quality. This drive was steered by that year’s program committee chair, Bethany Nowviskie, alongside 2014’s chair, Melissa Terras. Their reformative goals matched our current goals in this essay, and speak to a long history of experimentation and improvement efforts on behalf of ADHO. Their changes included making the conference more welcome to outsiders through ending policies that only insiders knew about; making the CFP less complex and easier to translate into multiple languages; taking reviewer language competencies into account systematically; and streamlining the submission and review process.
The biggest noticeable change to DH2013, however, was the institution of a reviewer bidding process and a phase of semi-open peer review. Peer reviewers were invited to read through and rank every submitted abstract according to how qualified they felt to review the abstract. Following this, the conference committee would match submissions to qualified peer reviewers, taking into account conflicts of interest. Submitting authors were invited to respond to reviews, and the committee would make a final decision based on the various reviews and rebuttals.This continues to be the process through DH2016. Changes continue to be made, most recently in 2016 with the addition of “Diversity” and “Multilinguality” as new keywords authors can append to their submissions.
While the list of submitted abstracts was private, accessible only to reviewers, as reviewers ourselves we had access to the submissions during the bidding phase. We used this access to create a dataset of conference submissions for DH2013, DH2014, and DH2015, which includes author names, affiliations, submission titles, author-selected topics, author-chosen keywords, and submission types (long paper, short paper, poster, panel).
We augmented this dataset by looking at the final conference programs in ‘13, ‘14, and ‘15, noting which submissions eventually made it onto the final conference program, and how they changed from the submission to the final product. This allows us to roughly estimate the acceptance rate of submissions, by comparing the submitted abstract lists to the final programs. It is not perfect, however, given that we don’t actually know whether submissions that didn’t make it to the final program were rejected, or if they were accepted and withdrawn. We also do not know who reviewed what, nor do we know the reviewers’ scores or any associated editorial decisions.
The original dataset, then, included fields for title, authors, author affiliations, original submission type, final accepted type, topics, keywords, and a boolean field for whether a submission made it to the final conference program. We cleaned the data up by merging duplicate people, ensuring e.g., if “Melissa Terras” was an author on two different submissions, she counted as the same person. For affiliations, we semi-automatically merged duplicate institutions, found the countries they reside in, and assigned those countries to broad UN regions. We also added data to the set, first automatically guessing a gender for each author, and then correcting the guesses by hand.
Given that abstracts were submitted to conferences with an expectation of privacy, we have not released the full submission dataset; we have, however, released the full dataset of final conference programs. 8
We would like to acknowledge the gross and problematic simplifications involved in this process of gendering authors without their consent or input. As Miriam Posner has pointed out, with regards to Getty’s Union List of Author Names, “no self-respecting humanities scholar would ever get away with such a crude representation of gender in traditional work”. And yet, we represent authors in just this crude fashion, labeling authors as male, female, or unknown/other. We did not encode changes of author gender over time, even though we know of at least a few authors in the dataset for whom this applies. We do not use the affordances of digital data to represent the fluidity of gender. This is problematic for a number of reasons, not least of which because, when we take a cookie cutter to the world, everything in the world will wind up looking like cookies.
We made this decision because, in the end, all data quality is contingent to the task at hand. It is possible to acknowledge an ontology’s shortcomings while still occasionally using that ontology to a positive effect. This is not always the case: often poor proxies get in the way a research agenda (e.g., citations as indicators of “impact” in digital humanities), rather than align with it. In the humanities, poor proxies are much more likely to get in the way of research than help it along, and afford the ability to make insensitive or reductivist decisions in the name of “scale”.
For example, in looking for ethnic diversity of a discipline, one might analyze last names as a proxy for country of origin, or analyze the color of recognized faces in pictures from recent conferences as a proxy for ethnic genealogy. Among other reasons, this approach falls short because ethnicity, race, and skin color are often not aligned, and last names (especially in the U.S.) are rarely indicative of anything at all. But they’re easy solutions, so people use them. These are moments when a bad proxy (and for human categories, proxies are almost universally bad) does not fruitfully contribute to a research agenda. As George E.P. Box put it, “all models are wrong, but some are useful.”
Some models are useful. Sometimes, the stars align and the easy solution is the best one for the question. If someone were researching immediate reactions of racial bias in the West, analyzing skin tone may get us something useful. In this case, the research focus is not someone’s racial identity, but someone’s race as immediately perceived by others, which would likely align with skin tone. Simply: if a person looks black, they’re more likely to be treated as such by the (white) world at large. 9
We believe our proxies, though grossly inaccurate, are useful for the questions of gender and geographic diversity and bias. The first step to improving DH conference diversity is noticing a problem; our data show that problem through staggeringly imbalanced regional and gender ratios. With regards to gender bias, showing whether reviewers are less likely to accept papers from authors who appear to be women can reveal entrenched biases, whether or not the author actually identifies as a woman. With that said, we invite future researchers to identify and expand on our admitted categorical errors, allowing everyone to see the contours of our community with even greater nuance.
The annual ADHO conference has grown significantly in the last fifteen years, as described in our companion piece 10, within which can be found a great discussion of our methods. This piece, rather than covering overall conference trends, focuses specifically on issues of diversity and acceptance rates. We cover geographic and gender diversity from 2000-2015, with additional discussions of topicality and peer review bias beginning in 2013.
Women comprise 36.1% of the 3,239 authors to DH conference presentations over the last fifteen years, counting every unique author only once. Melissa Terras’ names appears on 29 presentations between 200-2015, and Scott B. Weingart’s name appears on 4 presentations, but for the purpose of this metric each name counts only once. Female authorship representation fluctuates between 29%-38% depending on the year.
Weighting every authorship event individually (i.e., Weingart’s name counts 4 times, Terras’ 29 times), women’s representation drops to 32.7%. This reveals that women are less likely to author multiple pieces compared to their male counterparts. More than a third of the DH authorship pool are women, but fewer than a third of every name that appears on a presentation is a woman’s. Even fewer single-authored pieces are by a woman; only 29.8% of the 984 single-authored works between 2000-2015 female-authored. About a third (33.4%) of first authors on presentations are women. See Fig. 1 for a breakdown of these numbers over time. Note the lack of periodicity, suggesting gender representation is not affected by whether the conference is held in Europe or North America (until 2015, the conference alternated locations every year). The overall ratio wavers, but is neither improving nor worsening over time.
The gender disparity sparked controversy at DH2015 in Sydney. It was, however, at odds with a common anecdotal awareness that many of the most respected role-models and leaders in the community are women. To explore this disconnect, we experimented with using centrality in co-authorship networks as a proxy for fame, respectability, and general presence within the DH consciousness. We assume that individuals who author many presentations, co-author with many people, and play a central role in connecting DH’s disparate communities of authorship are the ones who are most likely to garner the respect (or at least awareness) of conference attendees.
We created a network of authors connected to their co-authors from presentations between 2000-2015, with ties strengthening the more frequently two authors collaborate. Of the 3,239 authors in our dataset, 61% (1,750 individuals) are reachable by one another via their co-authorship ties. For example, Beth Plale is reachable by Alan Liu because she co-authored with J. Stephen Downie, who co-authored with Geoffrey Rockwell, who co-authored with Alan Liu. Thus, 61% of the network is connected in one large component, and there are 299 smaller components, islands of co-authorship disconnected from the larger community.
The average woman co-authors with 5 other authors, and the average man co-authors with 5.3 other authors. The median number of co-authors for both men and women is 4. The average and median of several centrality measurements (closeness, betweenness, pagerank, and eigenvector) for both men and women are nearly equivalent; that is, any given woman is just as likely to be near the co-authorship core as any given man. Naturally, this does not imply that half of the most central authors are women, since only a third of the entire authorship pool are women. It means instead that gender does not influence one’s network centrality. Or at least it should.
The statistics show a curious trend for the most central figures in the network. Of the top 10 authors who co-author with the most others, 60% are women. Of the top 20, 45% are women. Of the top 50, 38% are women. Of the top 100, 32% are women. That is, the over half the DH co-authorship stars are women, but the further towards the periphery you look, the more men occupy the middle-tier positions (i.e., not stars, but still fairly active co-authors). The same holds true for the various centrality measurements: betweenness (60% women in top 10; 40% in top 20; 32% in top 50; 34% in top 100), pagerank (50% women in top 10; 40% in top 20; 32% in top 50; 28% in top 100), and eigenvector (60% women in top 10; 40% in top 20; 40% in top 50; 34% in top 100).
In short, half or more of the DH conference stars are women, but as you creep closer to the network periphery, you are increasingly likely to notice the prevailing gender disparity. This supports the mismatch between an anecdotal sense that women play a huge role in DH, and the data showing they are poorly represented at conferences. The results also match with the fact that women are disproportionately more likely to write about management and leadership, discussed at greater length below.
The heavily-male gender skew at DH conferences may lead one to suspect a bias in the peer review process. Recent data, however, show that if such a bias exists, it is not direct. Over the past three conferences, 71% of women and 73% of men who submitted presentations passed the peer review process. The difference is not great enough to rule out random chance (p=0.16 using χ²). The skew at conferences is more a result of fewer women submitting articles than of women’s articles not getting accepted. The one caveat, explained more below, is that certain topics women are more likely to write about are also less likely to be accepted through peer-review.
This does not imply a lack of bias in the DH community. For example, although only 33.5% of authors at DH2015 in Sydney were women, 46% of conference attendees were women. If women were simply uninterested in DH, the split in attendance vs. authorship would not be so high.
In regard to discussions of women in different roles in the DH community – less the publishing powerhouses and more the community leaders and organizers – the concept of the “glass cliff” can be useful. Research on the feminization of academia in Sweden uses the term “glass cliff” as a “metaphor used to describe a phenomenon when women are appointed to precarious leadership roles associated with an increased risk of negative consequences when a company is performing poorly and for example is experiencing profit falls, declining stock performance, and job cuts” (Peterson 2014, 4). The female academics (who also occupied senior managerial positions) interviewed in Helen Peterson’s study expressed concerns about increasing workloads, the precarity of their positions, and the potential for interpersonal conflict.
Institutional politics may also play a role in the gendered data here. Sarah Winslow says of institutional context that “female faculty are less likely to be located at research institutions or institutions that value research over teaching, both of which are associated with greater preference for research” (779). The research, teaching, and service divide in academia remains a thorny issue, especially given the prevalence of what has been called the pink collar workforce in academia, or the disproportionate amount of women working in low-paying teaching-oriented areas. This divide likely also contributed to differing gender ratios between attendees and authors at DH2015.
While the gendered implications of time allocation in universities are beyond the scope of this paper, it might be useful to note that there might be long-term consequences for how people spend their time interacting with scholarly tasks that extend beyond one specific institution. Winslow writes: “Since women bear a disproportionate responsibility for labor that is institution-specific (e.g., institutional housekeeping, mentoring individual students), their investments are less likely to be portable across institutions. This stands in stark contrast to men, whose investments in research make them more highly desirable candidates should they choose to leave their own institutions” (790). How this plays out specifically in the DH community remains to be seen, but the interdisciplinarity of DH along with its projects that span multiple working groups and institutions may unsettle some of the traditional bias that women in academia face.
Until 2015, the DH conference alternated every year between North America and Europe. As expected, until recently, the institutions represented at the conference have hailed mostly from these areas, with the primary locus falling in North America. In fact, since 2000, North American authors were the largest authorial constituency at eleven of the fifteen conferences, even though North America only hosted the conference seven times in that period.
With that said, as opposed to gender representation, national and institutional diversity is improving over time. Using an Index of Qualitative Variation (IQV), institutional variation begins around 0.992 in 2000 and ends around 0.996 in 2015, with steady increases over time. National IQV begins around 0.79 in 2010 and ends around 0.83 in 2015, also with steady increases over time. The most recent conference was the first that included over 30% of authors and attendees arriving from outside Europe or North America. Now that ADHO has implemented a three-year cycle, with every third year marked by a movement outside its usual territory, that diversity is likely to increase further still.
The most well-represented institutions are not as dominating as some may expect, given the common view of DH as a community centered around particular powerhouse departments or universities. The university with the most authors contributing to DH conferences (2.4% of the total authors) is King’s College London, followed by the Universities of Illinois (1.85%), Alberta (1.83%), and Virginia (1.75%). The most prominent university outside of North America or Europe is Ritsumeikan University, contributing 1.07% of all DH conference authors. In all, over a thousand institutions have contributed authors to the conference, and that number increases every year.
While these numbers represent institutional origins, the data available does not allow any further diving into birth countries, native language, ethnic identities, etc. The 2013-2015 dataset, including peer review information, does yield some insight into geography-influenced biases that may map to language or identity. While the peer review data do not show any clear bias by institutional country, there is a very clear bias against names which do not appear frequently in the U.S. Census or Social Security Index. We discovered this when attempting to statistically infer the gender of authors using these U.S.-based indices. 11 From 2013-2015, presentations written by those with names appearing frequently in these indices were significantly more likely to be accepted than those written by authors with non-English names (p < 0.0001). Whereas approximately 72% of authors with common U.S. names passed peer review, only 61% of authors with uncommon names passed. Without more data, we have no idea whether this tremendous disparity is due to a bias against popular topics from non-English-speaking countries, a higher likelihood of peer reviewers rejecting text written by non-native writers, an implicit bias by peer reviewers when they see “foreign” names, or something else entirely.
When submitting a presentation, authors are given the opportunity to provide keywords for their submission. Some keywords can be chosen freely, while others must be chosen from a controlled list of about 100 potential topics. These controlled keywords are used to help in the process of conference organization and peer reviewer selection, and they stay roughly constant every year. New keywords are occasionally added to the list, as in 2016, where authors can now select three topics which were not previously available: “Digital Humanities – Diversity”, “Digital Humanities – Multilinguality”, and “3D Printing”. The 2000-2015 conference dataset does not include keywords for every article, so this analysis will only cover the more detailed dataset, 2013-2015, with additional data on submissions for DH2016.
From 2013-2016, presentations were tagged with an average of six controlled keywords per submission. The most-used keywords are unsurprising: “Text Analysis” (tagged on 22% of submissions), “Data Mining / Text Mining” (20%), “Literary Studies” (20%), “Archives, Repositories, Sustainability And Preservation” (19%), and “Historical Studies” (18%). The most frequently-used keyword potentially pertaining directly to issues of diversity, “Cultural Studies”, appears on on 14% of submissions from 2013-2016. Only 2% of submissions are tagged with “Gender Studies”. The two diversity-related keywords introduced this year are already being used surprisingly frequently, with 9% of submissions in 2016 tagged “Digital Humanities – Diversity” and 6% of submissions tagged “Digital Humanities – Multilinguality”. With over 650 conference submissions for 2016, this translates to a reasonably large community of DH authors presenting on topics related to diversity.
Joining the topic and gender data for 2013-2015 reveals the extent to which certain subject matters are gendered at DH conferences. 12 Women are twice as likely to use the “Gender Studies” tag as male authors, whereas men are twice as likely to use the “Asian Studies” tag as female authors. Subjects related to pedagogy, creative / performing arts, art history, cultural studies, GLAM (galleries, libraries, archives, museums), DH institutional support, and project design/organization/management are more likely to be presented by women. Men, on the other hand, are more likely to write about standards & interoperability, the history of DH, programming, scholarly editing, stylistics, linguistics, network analysis, and natural language processing / text analysis. It seems DH topics have inherited the usual gender skews associated with the disciplines in which those topics originate.
We showed earlier that there was no direct gender bias in the peer review process. While true, there appears to be indirect bias with respect to how certain gendered topics are considered acceptable by the DH conference peer reviewers. A woman has just as much chance of getting a paper through peer review as a man if they both submit a presentation on the same topic (e.g., both women and men have a 72% chance of passing peer review if they write about network analysis, or a 65% chance of passing peer review if they write about knowledge representation), but topics that are heavily gendered towards women are less likely to get accepted. Cultural studies has a 57% acceptance rate, gender studies 60%, pedagogy 51%. Male-skewed topics have higher acceptance rates, like text analysis (83%), programming (80%), or Asian studies (79%). The female-gendering of DH institutional support and project organization also supports our earlier claim that, while women are well-represented among the DH leadership, they are more poorly represented in those topics that the majority of authors are discussing (programming, text analysis, etc.).
Regarding the clustering – and devaluing – of topics that women tend to present on at DH conferences, the widespread acknowledgement of the devaluing of women’s labor may help to explain this. We discussed the feminization of academia above, and indeed, this is a trend seen in practically all facets of society. The addition of emotional labor or caretaking tasks complicates this. Economist Teresa Ghilarducchi explains: “a lot of what women do in their lives is punctuated by time outside of the labor market — taking care of family, taking care of children — and women’s labor has always been devalued…[people] assume that she had some time out of the labor market and that she was doing something that was basically worthless, because she wasn’t being paid for it.” In academia specifically, the labyrinthine relationship of pay to tasks/labor further obscures value: we are rarely paid per task (per paper published or presented) on the research front; service work is almost entirely invisible; and teaching factors in with course loads, often with more up-front transparency for contingent laborers such as adjuncts and part-timers.
Our results seem to point to less of an obvious bias against women scholars than a subtler bias against topics that women tend to gravitate toward, or are seen as gravitating toward. This is in line with the concept of postfeminism, or the notion that feminism has met its main goals (e.g. getting women the right to vote and the right to an education), and thus is irrelevant to contemporary social needs and discourse. Thoroughly enmeshed in neoliberal discourse, postfeminism makes discussing misogyny seem obsolete and obscures the subtler ways in which sexism operates in daily life (Pomerantz, Raby, and Stefanik 2013). While individuals may or may not choose to identify as postfeminist, the overarching beliefs associated with postfeminism have permeated North American culture at a number of levels, leading us to posit the acceptance of the ideals of postfeminism as one explanation for the devaluing of topics that seem associated with women.
Discussion and Future Research
The analysis reveals an annual DH conference with a growing awareness of diversity-related issues, with moderate improvements in regional diversity, stagnation in gender diversity, and unknown (but anecdotally poor) diversity with regards to language, ethnicity, and skin color. Knowledge at the DH conference is heavily gendered, though women are not directly biased against during peer review, and while several prominent women occupy the community’s core, women occupy less space in the much larger periphery. No single or small set of institutions dominate the conference attendance, and though North America’s influence on ADHO cannot be understated, recent ADHO efforts are significantly improving the geographic spread of its constituency.
The DH conference, and by extension ADHO, is not the digital humanities. It is, however, the largest annual gathering of self-identified digital humanists, 13 and as such its makeup holds influence over the community at large. Its priorities, successes, and failures reflect on DH, both within the community and to the outside world, and those priorities get reinforced in future generations. If the DH conference remains as it is—devaluing knowledge associated with femininity, comprising only 36% women, and rejecting presentations by authors with non-English names—it will have significant difficulty attracting a more diverse crowd without explicit interventions. Given the shortcomings revealed in the data above, we present some possible interventions that can be made by ADHO or its members to foster a more diverse community, inspired by #WhatIfDH2016:
As pointed out by Yvonne Perkins, Ask presenters to include a brief “Collections Used” section, when appropriate. Such a practice would highlight and credit the important work being done by those who aren’t necessarily engaging in publishable research, and help legitimize that work to conference attendees.
As pointed out by Vika Zafrin, create guidelines for reviewers explicitly addressing diversity, and provide guidance on noticing and reducing peer review bias.
As pointed out by Vika Zafrin, community members can make an effort to solicit presentation submissions from women and people of color.
As pointed out by Vika Zafrin, collect and analyze data on who is peer reviewing, to see whether or the extent to which biases creep in at that stage.
As pointed out by Aimée Morrison, ensure that the conference stage is at least as diverse as the conference audience. This can be accomplished in a number of ways, from conference organizers making sure their keynote speakers draw from a broad pool, to organizing last-minute lightning lectures specifically for those who are registered but not presenting.
As pointed out by Christina Boyles, encourage the submission of research focused around the intersection of race, gender, and sexuality studies. This may be partially accomplished by including more topical categories for conference submissions, a step which ADHO has already taken for 2016.
As pointed out by many, take explicit steps in ensuring conference access to those with disabilities. We suggest this become an explicit part of the application package submitted by potential host institutions.
As pointed out by many, ensure the ease of participation-at-a-distance (both as audience and as speaker) for those without the resources to travel.
Give marginalized communities greater representation in the DH Conference peer reviewer pool. This can be done grassroots, with each of us reaching out to colleagues to volunteer as reviewers, and organizationally, perhaps by ADHO creating a volunteer group to seek out and encourage more diverse reviewers.
Consider the difference between diversifying (verb) vs. talking about diversity (noun), and consider whether other modes of disrupting hegemony, such as decolonization and queering, might be useful in these processes.
Contribute to the #whatifDH2016 and #whatifDH2017 discussions on twitter with other ideas for improvements.
Many options are available to improve representation at DH conferences, and some encouraging steps are already being taken by ADHO and its members. We hope to hear more concrete steps that may be taken, especially learned from experiences in other communities or outside of academia, in order to foster a healthier and more welcoming conference going forward.
In the interest of furthering these goals and improving the organizational memory of ADHO, the public portion of the data (final conference programs with full text and unique author IDs) is available alongside this publication [will link in final draft]. With this, others may test, correct, or improve our work. We will continue work by extending the dataset back to 1990, continuing to collect for future conferences, and creating an infrastructure that will allow the database to connect to others with similar collections. This will include the ability to encode more nuanced and fluid gender representations, and for authors to correct their own entries. Further work will also include exploring topical co-occurrence, institutional bias in peer review, how institutions affect centrality in the co-authorship network, and how authors who move between institutions affect all these dynamics.
The Digital Humanities will never be perfect. It embodies the worst of its criticisms and the best of its ideals, sometimes simultaneously. We believe a more diverse community will help tip those scales in the right direction, and present this chapter in service of that belief.
Peterson, Helen. “An Academic ‘Glass Cliff’? Exploring the Increase of Women in Swedish Higher Education Management.” Athens Journal of Education 1, no. 1 (February 2014): 32–44.
Pomerantz, Shauna, Rebecca Raby, and Andrea Stefanik. “Girls Run the World? Caught between Sexism and Postfeminism in the School.” *Gender & Society *27, no. 2 (April 1, 2013): 185-207. doi:10.1177/0891243212473199
Each author contributed equally to the final piece; please disregard authorship order. ↩
See Melissa Terras, “Disciplined: Using Educational Studies to Analyse ‘Humanities Computing.’” Literary and Linguistic Computing 21, no. 2 (June 1, 2006): 229–46. doi:10.1093/llc/fql022. Terras takes a similar approach, analyzing Humanities Computing “through its community, research, curriculum, teaching programmes, and the message they deliver, either consciously or unconsciously, about the scope of the discipline.” ↩
The authors have created a browsable archive of #whatifDH2016 tweets. ↩
Of the 146 presentations at DH2011, two standout in relation to diversity in DH: “Is There Anybody out There? Discovering New DH Practitioners in other Countries” and “A Trip Around the World: Balancing Geographical Diversity in Academic Research Teams.” ↩
See “Disrupting DH,” http://www.disruptingdh.com/ ↩
See Wernimont’s blog post, “No More Excuses” (September 2015) for more, as well as the Tumblr blog, “Congrats, you have an all male panel!” ↩
Miriam Posner offers a longer and more eloquent discussion of this in, “What’s Next: The Radical, Unrealized Potential of Digital Humanities.” Miriam Posner’s Blog. July 27, 2015. http://miriamposner.com/blog/whats-next-the-radical-unrealized-potential-of-digital-humanities/ ↩
[Link to the full public dataset, forthcoming and will be made available by time of publication]) ↩
We would like to acknowledge that race and ethnicity are frequently used interchangeably, though both are cultural constructs with their roots in Darwinian thought, colonialism, and imperialism. We retain these terms because they express cultural realities and lived experiences of oppression and bias, not because there is any scientific validity to their existence. For more on this tension, see John W.Burton, (2001), Culture and the Human Body: An Anthropological Perspective. Prospect Heights, Illinois: Waveland Press, 51-54. ↩
Weingart, S.B. & Eichmann, N. (2016). “What’s Under the Big Tent?: A Study of ADHO Conference Abstracts.” Manuscript submitted for publication. ↩
We used the process and script described in: Lincoln Mullen (2015). gender: Predict Gender from Names Using Historical Data. R package version 0.5.0.9000 (https://github.com/ropensci/gender) and Cameron Blevins and Lincoln Mullen, “Jane, John … Leslie? A Historical Method for Algorithmic Gender Prediction,” Digital Humanities Quarterly 9.3 (2015). ↩
For a breakdown of specific numbers of gender representation across all 96 topics from 2013-2015, see Weingart’s “Acceptances to Digital Humanities 2015 (part 4)”. ↩
While ADHO’s annual conference is usually the largest annual gathering of digital humanists, that place is constantly being vied for by the Digital Humanities Summer Institute in Victoria, Canada, which in 2013 boasted more attendees than DH2013 in Lincoln, Nebraska. ↩
[note: originally published as draft on March 17th, 2016]
DH2016 announced their final(ish) program yesterday and, of course, that means it’s analysis time. Every year, I steal scrape submission data from the reviewer interface, and then scrape the final conference program, to report acceptance rates and basic stats for the annual event. See my previous 7.2 million previous posts on the subject. Nobody gives me data, I take it (capta, amiright?), so take these results with as many grains of salt as you’ll find at the DH2016 salt mines.
As expected, this will be the biggest ADHO conference to date, continuing a mostly-consistent trend of yearly growth. Excluding workshops & keynotes, this year’s ADHO conference in Kraków, Poland will feature 417 posters & presentations, up from 259 in 2015 (an outlier, held in Australia) and the previous record of 345 in 2014 (Switzerland). At this rate, the number of DH presentations should surpass our human population by the year 2126 (or earlier in the case of unexpected zombies).
Acceptance rates this year are on par with previous years. An email from ADHO claims this year’s overall acceptance rate to be 62%, and my calculations put it at 64%. Previous years were within this range: 2013 Nebraska (64%), 2014 Switzerland (59%), and 2015 Australia (72%). Regarding form, the most difficult type of presentation to get through review is a long paper, with only 44% of submitted long papers being accepted as long papers. Another 7.5% of long papers were accepted as posters, and 10% as short papers. In total, 62% of long paper submissions were accepted in some form. Reviewers accepted 75% of panels and posters, leaving them mostly in their original form. The category least likely to get accepted in any form was the short paper, with an overall 59% acceptance rate (50% accepted as short papers; 8% accepted as posters). The moral of the story is that your best bet to get accepted to DH is to submit a poster. If you hate posters, submit a long paper; even if it’s not accepted as a long paper, it might still get in as a short or a poster. But if you do hate posters, maybe just avoid this conference.
About a third of this year’s presentations are single-authored, another third dual-authored, and the last third are authored by three or more people. As with 2013-2015, more authors means a more likely acceptance: reviewers accepted 51% of single-authored presentations, 66% of dual-authored presentations, and 74% of three-or-more-authored presentations.
Topically, the landscape of DH2016 will surprise few. A quarter of all presentations will involve text analysis, followed by historical studies (23% of all presentations), archives (21%), visualizations (20%), text/data mining (20%), and literary studies (20%). DH self-reflection is always popular, with this year’s hot-button issues being DH diversity (10%), DH pedagogy (10%), and DH facilities (7%). Surprisingly, other categories pertaining to pedagogy are also growing compared to previous years, though mostly it’s due to more submissions in that area. Reviewers still don’t rate pedagogy presentations very highly, but more on that in the next post. Some topical low spots compared to previous years include social media (2% of all presentations), anthropology (3%), VR/AR (3%), crowdsourcing (4%), and philosophy (5%).
This year will likely be the most linguistically diverse conference thus-far: 92% English, 7% French, 0.5% German, with other presentations in Spanish, Italian, Polish, etc. (And by “most linguistically diverse” obviously I mean “really not very diverse but have you seen the previous conferences?”) Submitting in a non-English language doesn’t appreciably affect acceptance rates.
That’s all for now. Stay-tuned for Pt. 2, with more thorough comparisons to previous years, actual granular data/viz on topics, analyses of gender and geography, as well as interpretations of what the changing landscape means for DH.
Twice a year I indulge my meta-disciplinary sweet tooth: once to look at who’s submitting what to ADHO’s annual digital humanities conference, and once to look at which pieces get accepted (see the rest of the series). This post presents my first look at DH2016 conference submissions, the data for which I scraped from ConfTool during the open peer review bidding phase. Open peer review bidding began in 2013, so I have 4 years of data. I opt not to publish this data, as most authors submit pieces under an expectation of privacy, and might violently throw things at my face if people find out which submissions weren’t accepted. Also ethics.
Submission Numbers & Types
The basic numbers: 652 submissions (268 long papers, 223 short papers, 33 panels / multiple paper sessions, 128 posters). For those playing along at home, that’s:
2013 Nebraska: 348 (144/118/20/66)
2014 Lausanne: 589 (250/198/30/111)
2015 Sydney: 360 (192/102/13/53)
2016 Kraków: 652 (268/223/33/128)
DH2016 submissions are on par to continue the consistent-ish trend of growth every year since 1999, the large dip in 2015 unsurprising given its very different author pool, and the fact that it was the first time the conference visited the southern hemisphere or Asia-Pacific. The different author pool in 2015 also likely explains why it was the only conference to deviate from the normal submission-type ratios.
Regarding co-authorship, the number has shifted this year, though not enough to pass any significance tests.
DH2016 has proportionally slightly fewer single authored papers than previous years, and slightly more 2-, 3-, and 4-authored papers. One submission has 17 authors (not quite the 5,154-author record of high energy physics, but we’re getting there, eh?), but mostly it’s par for the course here.
Topically, DH2016 submissions continue many trends seen previously.
Authors must tag their submissions into multiple categories, or topics, using a controlled vocabulary. The figure presents a list of topics tagged to submissions, ordered top-to-bottom by the largest proportion of submissions with a certain tag for 2016. Nearly 25% of DH2016 submissions, for example, were tagged with “Text Analysis”. The dashed lines represent previous years’ tag proportions, with the darkest representing 2015, getting lighter towards 2013. New topics, those which just entered the controlled vocabulary this year, are listed in red. They are 3D Printing, DH Multilinguality, and DH Diversity.
Scroll past the long figure below to read my analysis:
In a reveal that will shock all species in the known universe, text analysis dominates DH2016 submissions—the proportion even grew from previous years. Text & data mining, archives, and data visualization aren’t far behind, each growing from previous years.
What did actually (pleasantly) surprise me was that, for the first time since I began counting in 2013, history submissions outnumber literary ones. Compare this to 2013, when literary studies were twice as well represented as historical. Other top-level categories experiencing growth include: corpus studies, content analysis, knowledge representation, NLP, and linguistics.
Two areas which I’ve pointed out previously as needing better representation, geography and pedagogy, both grew compared to previous years. I’ve also pointed out a lack of discussion of diversity, but part of that lack was that authors had no “diversity” category to label their research with—that is, the issue I pointed out may have been as much a problem with the topic taxonomy as with the research itself. ADHO added “Diversity” and “Multilinguality” as potential topic labels this year, which were tagged to 9.4% and 6.5% of submissions, respectively. One-in-ten submissions dealing specifically with issues of diversity is encouraging to see.
Unsurprisingly, since Sydney, submissions tagged “Asian Studies” have dropped. Other consistent drops over the last few years include software design, A/V & multimedia (sadface), information retrieval, XML & text encoding, internet & social media-related topics, crowdsourcing, and anthropology. The conference is also getting less self-referential, with a consistent drop in DH histories and meta-analyses (like this one!). Mysteriously, submissions tagged with the category “Other” have dropped rapidly each year, suggesting… dunno, aliens?
I have the suspicion that some numbers are artificially growing because there are more topics tagged per article this year than previous years, which I’ll check and report on in the next post.
It may be while before I upload the next section due to other commitments. In the meantime, you can fill your copious free-time reading earlier posts on this subject or my recent book with Shawn Graham & Ian Milligan, The Historian’s Macroscope. Maybe you can buy it for your toddler this holiday season. It fits perfectly in any stocking (assuming your stockings are infinitely deep, like Mary Poppins’ purse, which as a Jew watching Christmas from afar I just always assume is the case).
I’ve had a blast since starting as Digital Humanities Specialist at CMU. Enough administrators, faculty, and students are on board to make building a DH strength here pretty easy, and we’re neighbors to Pitt DHRX, a really supportive supercomputing center, and great allies in the Mayor’s Office keen on a city rich with art, data, and both combined.
We want a developer to help jump-start our research efforts. You’ll be working as a full collaborator on projects from all sorts of domains, and as a review board member you’ll have a strong say in which projects they are and how they get implemented. You and I will work together in achievable rapid prototyping, analyzing data, and web deployment.
The idea is we build or do stuff that’s scholarly, interesting, and can have a proof-of-concept or article done in a semester or two. With that, the project can go on to seek additional funding and a full-time specialized programmer, or we can finish there and all be proud authors or creators of something we enjoyed making.
Ideally, you have a social science, humanities, journalism, or similar research background, and the broad tech chops to create a d3 viz, DeepDream some dogs into a work of art, manage a NoSQL database, and whatever else seems handy. Ruby on Rails, probably.
We’re looking for someone who loves playing with new tech stacks, isn’t afraid to get their hands dirty, and knows how to talk to humans. You probably have a static site and a github account. You get excited by interactive data stories, and want to make them with us. This job values breadth over depth and done over perfect.
The job isn’t as insane as it sounds—you don’t actually need to be able to do all this already, just be the sort of person who can learn on the fly. A bachelor’s degree or similar experience is required, with a strong preference for candidates with some research background. You’ll need to submit or point to some examples of work you’ve done.
We’re an equal opportunity employer, and would love to see applications from women, minorities, or other groups who often have a tough time getting developer jobs. If you work here you can take two free classes a semester. Say, who wants a fancy CMU computer science graduate degree? We can offer an awesome city, friendly coworkers, and a competitive salary (also Pittsburgh’s cheap so you wouldn’t live in a closet, like in SF or NYC).
The ad, if you’re too lazy to click the link, or are scared CMU hosts viruses:
Digital Humanities Developer, Dietrich College of Humanities and Social Sciences
Summary The Dietrich College of Humanities and Social Sciences at Carnegie Mellon University (CMU) is undertaking a long-term initiative to foster digital humanities research among its faculty, staff, and students. As part of this initiative, CMU seeks an experienced Developer to collaborate on cutting edge interdisciplinary projects.
CMU is a world leader in technology-oriented research, and a highly supportive environment for cross-departmental teams. The Developer would work alongside researchers from Dietrich and elsewhere to plan and implement digital humanities projects, from statistical analyses of millions of legal documents to websites that crowdsource grammars of endangered languages. Located in the the Office of The Dean under CMU’s Digital Humanities Specialist, the developer will help start up faculty projects into functioning prototypes where they can acquire sustaining funding to hire specialists for more focused development.
The position emphasizes rapid, iterative deployment and the ability to learn new techniques on the job, with a focus on technologies intersecting data science and web development, such as D3.js, NoSQL, Shiny (R), IPython Notebooks, APIs, and Ruby on Rails. Experience with digital humanities or computational social sciences is also beneficial, including work with machine learning, GIS, or computational linguistics.
The individual in this position will work with clients and the digital humanities specialist to determine achievable short-term prototypes in web development or data analysis/presentation, and will be responsible for implementing the technical aspects of these goals in a timely fashion. As a collaborator, the Digital Humanities Developer will play a role in project decision-making, where appropriate, and will be credited on final products to which they extensively contribute.
Please submit a cover letter, phone numbers and email addresses for two references, a résumé or cv, and a page describing how your previous work fits the job, including links to your github account or other relevant previous work examples.
Bachelor’s Degree in humanities computing, digital humanities, informatics, computer science, related field, or equivalent combination of training and experience.
At least one year of experience in modern web development and/or data science, preferably in a research and development team setting.
Some system administration.
Advanced degree in digital humanities, computational social science, informatics, or data science. Coursework in data visualization, machine learning, statistics, or MVC web applications.
Three or more years at the intersection of web development/deployment and machine learning (e.g. data journalism or digital humanities) in an agile software environment.
Ability to assess client needs and offer creative research or publication solutions.
Any combination of GIS, NLTK, statistical models, ABMs, web scraping, mahout/hadoop, network analysis, data visualization, RESTful services, testing frameworks, XML, HPC.
Job Function: Research Programming
Primary Location: United States-Pennsylvania-Pittsburgh
Time Type: Full Time
Organization: DIETRICH DEAN’S OFFICE
Minimum Education Level: Bachelor’s Degree or equivalent
Musings on materiality and cost after a tour of The Shoah Foundation.
Forgetting The Holocaust
As the only historian in my immediate family, I’m responsible for our genealogy, saved in a massive GEDCOM file. Through the wonders of the web, I now manage quite the sprawling tree: over 100,000 people, hundreds of photos, thousands of census records & historical documents. The majority came from distant relations managing their own trees, with whom I share.
Such a massive well-kept dataset is catnip for a digital humanist. I can analyze my family! The obvious first step is basic stats, like the most common last name (Aber), average number of kids (2), average age at death (56), or most-frequently named location (New York). As an American Jew, I wasn’t shocked to see New York as the most-common place name in the list. But I was unprepared for the second-most-common named location: Auschwitz.
I’m lucky enough to write this because my great grandparents all left Europe before 1915. My grandparents don’t have tattoos on their arms or horror stories about concentration camps, though I’ve met survivors their age. I never felt so connected to The Holocaust, HaShoah, until I took time to see explore the hundreds of branches of my family tree that simply stopped growing in the 1940s.
I grew up hearing “Never Forget” as a mantra to honor the 11 million victims of hate and murder at the hands of Nazis, and to ensure it never happens again. That a Canadian official has forgotten—that we have all forgotten many of the other genocides that haunt human history—suggests how easy it is to forget. And how much work it is to remember.
The material cost of remembering 50,000 Holocaust survivors & witnesses
Yad Vashem (“a place and a name”) represents the attempt to inscribe, preserve, and publicize the names of Jewish Holocaust victims who have no-one to remember them. Over four million names have been collected to date.
The USC Shoah Foundation, founded by Steven Spielberg in 1994 to remember Holocaust survivors and witnesses, is both smaller and larger than Yad Vashem. Smaller because the number of survivors and witnesses still alive in 1994 numbered far fewer than Yad Vashem‘s 4.3 million; larger because the foundation conducted video interviews: 100,000 hours of testimony from 50,000 individuals, plus recent additions of witnesses and survivors of other genocides around the world. Where Yad Vashem remembers those killed, the Shoah Foundation remembers those who survived. What does it take to preserve the memories of 50,000 people?
I got a taste of the answer to that question at a workshop this week hosted by USC’s Digital Humanities Program, who were kind enough to give us a tour of the Shoah Foundation facilities. Sam Gustman, the foundation’s CTO and Associate Dean of USC’s Libraries, gave the tour.
Digital preservation it a complex process. In this case, it began by digitizing 235,000 analog Betacam SP Videocassettes, on which the original interviews had been recorded, a process which took from 2008-2012. This had to be done quickly (automatically/robotically), given that cassette tapes are prone to become sticky, brittle, and unplayable within a few decades due to hydrolysis. They digitized about 30,000 hours per year. The process eventually produced 8 petabytes (link to more technical details) of lossless JPEG 2000 videos, roughly the equivalent of 2 million DVDs. Stacked on top of each other, those DVDs would reach three times higher than Burj Khalifa, the world’s tallest tower.
From there, the team spent quite some time correcting errors that existed in the original tapes, and ones that were introduced in the process of digitization. They employed a small army of signal processing students, patented new technologies for automated error detection & processing/cleaning, and wound up cleaning video from about 12,000 tapes. According to our tour guide, cleaning is still happening.
Lest you feel safe knowing that digitization lengthens the preservation time, turns out you’re wrong. Film lasts longer than most electronic storage, but making film copies would have cost the foundation $140,000,000 and made access incredibly difficult. Digital copies would only cost tens of millions of dollars, even though hard-drives couldn’t be trusted to last more than a decade. Their solution was a RAID hard-drive system in an Oracle StorageTek SL8500 (of which they have two), and a nightly process of checking video files for even the slightest of errors. If an error is found, a backup is loaded to a new cartridge, and the old cartridge is destroyed. Their two StorageTeks each fit over 10,000 drive cartridges, have 55 petabytes worth of storage space, weigh about 4,000 lbs, and are about the size of a New York City apartment. If a drive isn’t backed up and replaced within three years, they throw it out and replace it anyway, just in case. And this setup apparently saved the Shoah Foundation $6 million.
Oh, and they have another facility a few states away, connected directly via high-bandwidth fiber optic cables, where everything just described is duplicated in case California falls into the ocean.
So how much does it cost to remember 50,000 Holocaust witnesses and survivors for, say, 20 years? I mean, above and beyond the cost of building a cutting edge facility, developing new technologies of preservation, cooling and housing a freight container worth of hard drives, laying fiber optic cables below ground across several states, etc.? I don’t know. But I do know how much the Shoah Foundation would charge you to save 8 petabytes worth of videos for 20 years, if you were a USC Professor. They’d charge you $1,000/TB/20 years.
The Foundation’s videos take up 8,000 terabytes, which at $1,000 each would cost you $8 million per 20 years, or about half a million dollars per year. Combine that with all the physical space it takes up, and never forgetting the Holocaust is sounding rather prohibitive. And what about after 20 years, when modern operating systems forget how to read JPEG 2000 or interface with StorageTek T10000C Tape Drives, and the Shoah Foundation needs to undertake another massive data conversion? I can see why that Canadian official didn’t manage it.
The Reconcentration of Holocaust Survivors
While I appreciated the guided tour of the exhibit, and am thankful for the massive amounts of money, time, and effort scholars and donors are putting into remembering Holocaust survivors, I couldn’t help but be creeped out by the experience.
Our tour began by entering a high security facility. We signed our names on little pieces of paper and were herded through several layers of locked doors and small rooms. Not quite the way one expects to enter the project tasked with remembering and respecting the victims of genocide.
The Nazi’s assembly-line techniques for mass extermination led to starkly regular camps, like Auschwitz pictured above, laid out in efficient grids for the purpose of efficient control and killings. “Concentration camp”, by the way, refers to the concentration of people into small spaces, coming from “reconcentration camps” in Cuba. Now we’re concentrating 50,000 testimonies into a couple of closets with production line efficiency, reconcentrating the stories of people who dispersed across the world, so they’re all in one easy-to-access place.
We’ve squeezed 100,000 hours of testimony into a server farm that consists of a series of boxes embedded in a series of larger boxes, all aligned to a grid; input, output, and eventual destruction of inferior entities handled by robots. Audits occur nightly.
The Shoah Foundation materials were collected, developed, and preserved with the utmost respect. The goal is just, the cause respectable, and the efforts incredibly important. And by reconcentrating survivors’ stories, they can now be accessed by the world. I don’t blame the Foundation for the parallels which are as much a construct of my mind as they are of the society in which this technology developed. Still, on Halloween, it’s hard to avoid reflecting on the material, monetary, and ultimately dehumanizing costs of processing ghosts into the machine.