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.
The Matthew Effect isn’t the reality TV show it sounds like.
For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken even that which he hath. —Matthew 25:29, King James Bible.
It’s the Biblical idea that the rich get richer, and it’s become a popular party trick among sociologists (yes, sociologists go to parties) describing how society works. In academia, the phrase is brought up alongside evidence that shows previous grant-recipients are more likely to receive new grants than their peers, and the more money a researcher has been awarded, the more they’re likely to get going forward.
The Matthew Effect is also employed metaphorically, when it comes to citations. He who gets some citations will accrue more; she who has the most citations will accrue them exponentially faster. There are many correct explanations, but the simplest one will do here:
If Susan’s article on the danger of velociraptors is cited by 15 other articles, I am more likely to find it and cite her than another article on velociraptors containing the same information, that has never been 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.
Since faculty jobs are stagnating compared to adjunct work, yet new PhDs are being granted faster than new jobs become available, we are presented with the much-discussed crisis in higher education. Don’t worry, we’re told, academia is a meritocracy. With so few jobs, only the cream of the crop will get them. The best work will still be done, even in these hard times.
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 Philosophy had players move figures around a board, inching closer to living a luxurious life in the shadow of a rich patron. Random chance ruled their progression—a role of the dice—and occasionally they’d reach a tile that said things like: “Your patron dies, go back 5 squares”.
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.
We want an answer to the question of whether positive feedback loops and a scarce economy are sufficient to prevent the possibility of a meritocracy. In 1971, Tom Schelling asked an unrelated question which he answered using a very relevant method: can racial segregation manifest in a community whose every actor is intent on not living a segregated life? Spoiler alert: yes.
He answered this question using by simulating an artificial world—similar in spirit to the Columbia social music experiment, except for using real participants, he experimented on very simple rule-abiding game creatures of his own invention. A bit like having a computer play checkers against itself.
The experiment is simple enough: a bunch of creatures occupy a checker board, and like checker pieces, they’re red or black. Every turn, one creature has the opportunity to move randomly to another empty space on the board, and their decision to move is based on their comfort with their neighbors. Red pieces want red neighbors, and black pieces want black neighbors, and they keep moving randomly ’till they’re all comfortable. Unsurprisingly, segregated creature communities appear in short order.
What if we our checker-creatures were more relaxed in their comforts? They’d be comfortable as long as they were in the majority; say, at least 50% of their neighbors were the same color. Again, let the computer play itself for a while, and within a few cycles the checker board is once again almost completely segregated.
What if the checker pieces are excited about the prospect of a diverse neighborhood? We relax the criteria even more, so red checkers only move if fewer than a third of their neighbors are red (that is, they’re totally comfortable with 66% of their neighbors being black)? If we run the experiment again, we see, again, the checker board breaks up into segregated communities.
Schelling’s claim wasn’t about how the world worked, but about what the simplest conditions were that could still explain racism. In his fictional checkers-world, every piece could be generously interested in living in a diverse neighborhood, and yet the system still eventually resulted in segregation. This offered a powerful support for the theory that racism could operate subtly, even if every actor were well-intended.
Vi Hart and Nicky Case created an interactive visualization/game that teaches Schelling’s segregation model perfectly. Go play it. Then come back. I’ll wait.
Such an experiment can be devised for our 41st-chair/positive-feedback system as well. We can even build a simulation whose rules match the Academic Musical Chairs I described above. All we need to do is show that a system in which both effects operate (a fact empirically proven time and again in academia) produces fundamental challenges for meritocracy. Such a model would be show that simple meritocratic intent is insufficient to produce a meritocracy. Hulk smashing the myth of the meritocracy seems fun; I think I’ll get started soon.
The Social Network
Our world ain’t that simple. For one, as seen in Academic Musical Chairs, your place in the social network influences your chances of success. A heavy-hitting advisor, an old-boys cohort, etc., all improve your starting position when you begin the game.
To put it more operationally, let’s go back to the Columbia social music experiment. Part of a song’s success was due to quality, but the stuff that made stars was much more contingent on chance timing followed by positive feedback loops. Two of the authors from the 2006 study wrote another in 2007, echoing this claim that good timing was more important than individual influence:
models of information cascades, as well as human subjects experiments that have been designed to test the models (Anderson and Holt 1997; Kubler and Weizsacker 2004), are explicitly constructed such that there is nothing special about those individuals, either in terms of their personal characteristics or in their ability to influence others. Thus, whatever influence these individuals exert on the collective outcome is an accidental consequence of their randomly assigned position in the queue.
These articles are part of a large literature in predicting popularity, viral hits, success, and so forth. There’s The Pulse of News in Social Media: Forecasting Popularity by Bandari, Asur, & Huberman, which showed that a top predictor of newspaper shares was the source rather than the content of an article, and that a major chunk of articles that do get shared never really make it to viral status. There’s Can Cascades be Predicted? by Cheng, Adamic, Dow, Kleinberg, and Leskovec (all-star cast if ever I saw one), which shows the remarkable reliance on timing & first impressions in predicting success, and also the reliance on social connectivity. That is, success travels faster through those who are well-connected (shocking, right?), and structural properties of the social network are important. This study by Susarla et al. also shows the importance of location in the social network in helping push those positive feedback loops, effecting the magnitude of success in YouTube Video shares.
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!
One of the only variables I've ever seen that truly predicts grant success … your application number pic.twitter.com/R7Q3k8PNck
— Adrian Barnett (@aidybarnett) March 19, 2016
Addendum for Historians
You thought I was done?
As a historian of science, this situation has some interesting repercussions for my research. Perhaps most importantly, it and related concepts from Complex Systems research offer a middle ground framework between environmental/contextual determinism (the world shapes us in fundamentally predictable ways) and individual historical agency (we possess the power to shape the world around us, making the world fundamentally unpredictable).
More concretely, it is historically fruitful to ask not simply what non-“scientific” strategies were employed by famous scientists to get ahead (see Biagioli’s Galileo, Courtier), but also what did or did not set those strategies apart from the masses of people we no longer remember. Galileo, Courtier provides a great example of what we historians can do on a larger scale: it traces Galileo’s machinations to wind up in the good graces of a wealthy patron, and how such a system affected his own research. Using recently-available data on early modern social and scholarly networks, as well as the beginnings of data on people’s activities, interests, practices, and productions, it should be possible to zoom out from Biagioli’s viewpoint and get a fairly sophisticated picture of trajectories and practices of people who weren’t Galileo.
This is all very preliminary, just publicly blogging whims, but I’d be fascinated by what a wide-angle (dare I say, macroscopic?) analysis of the 41st chair in could tell us about how social and “scientific” practices shaped one another in the 16th and 17th centuries. I believe this would bear previously-impossible fruit, since a lone historian grasping ten thousand tertiary actors at once is a fool’s errand, but is a walk in the park for my laptop.
As this really is whim-blogging, I’d love to hear your thoughts.