9 03 2014
This post is mostly just thinking out loud, musing about two related barriers to scholarship: a stigma related to self-plagiarism, and various copyright concerns. It includes a potential way to get past them.
When Jonah Lehrer’s plagiarism scandal first broke, it sounded a bit silly. Lehrer, it turned out, had taken some sentences he’d used in earlier articles, and reused them in a few New Yorker blog posts. Without citing himself. Oh no, I thought. Surely, this represents the height of modern journalistic moral depravity.
Of course, later it was revealed that he’d bent facts, and plagiarized from others without reference, and these were all legitimately upsetting. And plagiarizing himself without reference was mildly annoying, though certainly not something that should have attracted national media attention. But it raises an interesting question: why is self-plagiarism wrong? And it’s as wrong in academia as it is in journalism.
I can’t speak for journalists (though Alberto Cairo can, and he lists some of the good reasons why non-referenced self-plagiarism is bad and links to not one, but two articles about it, and), but for academia, the reasons behind the wrongness seem pretty clear.
Lehrer chart from Slate. [via]
- It’s wrong to directly lift from any source without adequate citation. This only applies to non-cited self-plagiarism, obviously.
- It’s wrong to double-dip. The currency of the academy is publications / CV lines, and if you reuse work to fill your CV, you’re getting an unfair advantage.
- Confusion. Which version should people reference if you have so many versions of a similar work?
- Copyright. You just can’t reuse stuff, because your previous publishers own the copyright on your earlier work.
That about covers it. Let’s pretend academics always cite their own works (because, hell, it gives them more citations), so we can do away with #1. Regular readers will know my position on publisher-owned copyright, so I just won’t get into #4 here to save you my preaching. The others are a bit more difficult to write off, but before I go on to try to do that, I’d like to talk a bit about my own experience of self-plagiarism as a barrier to scholarship.
I was recently invited to speak at the Universal Decimal Classification seminar, where I presented on the history of trees as a visual metaphor for knowledge classification. It’s not exactly my research area, but it was such a fun subject, I’ve decided to write an article about it. The problem is, the proceedings of the UDC seminar were published, and about 50% of what I wanted to write is already sitting in a published proceedings that, let’s face it, not many people will ever read. And if I ever want to add to it, I have to change the already-published material significantly if I want to send it out again.
Since I presented, my thesis has changed slightly, I’ve added a good chunk of more material, and I fleshed out the theoretical underpinnings. I now have a pretty good article that’s ready to be sent out for peer review, but if I want to do that, I can’t just have a reference saying “half of this came from a published proceeding.” Well, I could, but apparently there’s a slight taboo against this. I was told to “be careful,” that I’d have to “rephrase” and “reword.” And, of course, I’d have to cite my earlier publication.
I imagine most of this comes from the fear of scholars double-dipping, or padding their CVs. Which is stupid. Good scholarship should come first, and our methods of scholarly attribution should mold itself to it. Right now, scholarship is enslaved to the process of attribution and publication. It’s why we willingly donate our time and research to publishing articles, and then have our universities buy back our freely-given scholarship in expensive subscription packages, when we could just have the universities pay for the research upfront and then release it for free.
The question of copyright is pretty clear: how much will the publisher charge if I want my to reuse a significant portion of my work somewhere else? The publisher to which I refer, Ergon Verlag, I’ve heard is pretty lenient about such things, but what if I were reprinting from a different publish?
There’s an additional, more external, concern about my materials. It’s a history of illustrations, and the manuscript itself contains 48 illustrations in all. If I want to use them in my article, for demonstrative purposes, I not only need to cite the original sources (of course), I need to get permission to use the illustrations from the publishers who scanned them – and this can be costly and time consuming. I priced a few of them so-far, and they range from free to hundreds of dollars.
A Potential Solution – Iterative Writing
To recap, there are two things currently preventing me from sending out a decent piece of scholarship for peer-review:
- A taboo against self-plagiarism, which requires quite a bit of time for rewriting, permission from the original publisher to reuse material, and/or the dissolution of such a taboo.
- The cost and time commitment of tracking down copyright holders to get permission to reproduce illustrations.
I believe the first issue is largely a historical artifact of print-based media. Scholars have this sense of citing the source because, for hundreds of years, nearly every print of a single text was largely identical. Sure, there were occasionally a handful of editions, some small textual changes, some page number changes, but citing a text could easily be done, and so we developed a huge infrastructure around citations and publications that exists to this day. It was costly and difficult to change a printed text, and so it wasn’t done often, and now our scholarly practices are based around the idea scholarly material has to be permanent and unchanging, finished, if they are to enter into the canon and become citeable sources.
In the age of Wikipedia, this is a weird idea. Texts grow organically, they change, they revert. Blog posts get updated. A scholarly article, though, is relatively constant, even those in online-only publications. One of the major exceptions are ArXiv-like pre-print repositories, which allow an article to go through several versions before the final one goes off to print. But generally, once the final version goes to print, no further changes are made.
The reasons behind this seem logical: it’s the way we’ve always done it, so why change a good thing? It’s hard to cite something that’s constantly changing; how do we know the version we cited will be preserved?
In an age of cheap storage and easily tracked changes, this really shouldn’t be a concern. Wikipedia does this very well: you can easily cite the version of an article from a specific date and, if you want, easily see how the article changed between then and any other date.
Changes between versions of the Wikipedia entry on History.
This would be more difficult to implement in academia because article hosting isn’t centralized. It’s difficult to be certain that the URL hosting a journal article now will persist for 50 years, both because of ownership and design changes, and it’s difficult to trust that whomever owns the article or the site won’t change the content and not preserve every single version, or a detailed description of changes they’ve made.
There’s an easy solution: don’t just reference everything you cite, embed everything you cite. If you cite a picture, include the picture. If you cite a book, include the book. If you cite an article, include the article. Storage is cheap: if your book cites a thousand sources, and includes a copy of every single one, it’ll be at most a gigabyte. Probably, it would be quite a deal smaller. That way, if the material changes down the line, everyone reading your research will till be able to refer to the original material. Further, because you include a full reference, people can go and look the material up to see if it has changed or updated in the time since you cited it.
Of course, this idea can’t work – copyright wouldn’t let it. But again, this is a situation where the industry of academia is getting in the way of potential improvements to the way scholarship can work.
The important thing, though, is that self-plagiarization would become a somewhat irrelevant concept. Want to write more about what you wrote before? Just iterate your article. Add some new references, a paragraph here or there, change the thesis slightly. Make sure to keep a log of all your changes.
I don’t know if this is a good solution, but it’s one of many improvements to scholarship – or at least, a removal of barriers to publishing interesting things in a timely and inexpensive fashion – which is currently impossible because of copyright concerns and institutional barriers to change. Cameron Neylon, from PLOS, recently discussed how copyright put up some barriers to his own interesting ideas. Academia is not a nimble beast, and because of it, we are stuck with a lot of scholarly practices which are, in part, due to the constraints of old media.
In short: academic writing is tough. There are ways it could be easier, that would allow good scholarship to flow more freely, but we are constrained by path dependency from choices we made hundreds of years ago. It’s time to be a bit more flexible and be more willing to try out new ideas. This isn’t anywhere near a novel concept on my part, but it’s worth repeating.
The last big barrier to self-plagiarism, double dipping to pad one’s CV, still seems tricky to get past. I’m not thrilled with the way we currently assess scholarship, and “CV size” is just one of the things I don’t like about it, but I don’t have any particularly clever fixes on that end.
23 02 2014
We interrupt this usually-DH blog because I got in a discussion about Special Relativity with a friend, and promised it was easily understood using only the math we use for triangles. But I’m a historian, so I can’t leave a good description alone without some background.
If you just want to learn how relativity works, skip ahead to the next post, Relativity Made Simple [Note! I haven't written it yet, this is a two-part post. Stay-tuned for the next section]; if you hate science and don’t want to know how the universe functions, but love history, read only this post. If you have a month of time to kill, just skip this post entirely and read through my 122-item relativity bibliography on Zotero. Everyone else, disregard this paragraph.
An Oddly Selective History of Relativity
This is not a history of how Einstein came up with his Theory of Special Relativity as laid out in Zur Elektrodynamik bewegter Körper in 1905. It’s filled with big words like aberration and electrodynamics, and equations with occult symbols. We don’t need to know that stuff. This is a history of how others understood relativity. Eventually, you’re going to understand relativity, but first I’m going to tell you how other people, much smarter than you, did not.
There’s an infamous (potentially mythical) story about how difficult it is to understand relativity: Arthur Eddington, a prominent astronomer, was asked whether it was true that only three people in the world understood relativity. After pausing for a moment, Eddington replies “I’m trying to think who the third person is!” This was about General Relativity, but it was also a joke: good scientists know relativity isn’t incredibly difficult to grasp, and even early on, lots of people could claim to understand it.
Good historians, however, know that’s not the whole story. It turns out a lot of people who thought they understood Einstein’s conceptions of relativity actually did not, including those who agreed with him. This, in part, is that story.
Relativity Before Einstein
Einstein’s special theory of relativity relied on two assumptions: (1) you can’t ever tell whether you’re standing still or moving at a constant velocity (or, in physics-speak, the laws of physics in any inertial reference frame are indistinguishable from one another), and (2) light always looks like it’s moving at the same speed (in physics-speak, the speed of light is always constant no matter the velocity of the emitting body nor that of the observer’s inertial reference frame). Let’s trace these concepts back.
Our story begins in the 14th century. William of Occam, famous for his razor, claimed motion was merely the location of a body and its successive positions over time; motion itself was in the mind. Because position was simply defined in terms of the bodies that surround it, this meant motion was relative. Occam’s student, Buridan, pushed that claim forward, saying “If anyone is moved in a ship and imagines that he is at rest, then, should he see another ship which is truly at rest, it will appear to him that the other ship is moved.”
The story movies forward at irregular speed (much like the speed of this blog, and the pacing of this post). Within a century, scholars introduced the concepts of an infinite universe without any center, nor any other ‘absolute’ location. Copernicus cleverly latched onto this relativistic thinking by showing that the math works just as well, if not better, when the Earth orbits the Sun, rather than vice versa. Galileo claimed there was no way, on the basis of mechanical experiments, to tell whether you were standing still or moving at a uniform speed.
Galileo’s relativity [via]. The site where this comes from is a little crazy, but the figure is still useful, so here it is.
For his part, Descartes disagreed, but did say that the only way one could discuss movement was relative to other objects. Christian Huygens takes Descartes a step forward, showing that there are no ‘privileged’ motions or speeds (that is, there is no intrinsic meaning of a universal ‘at rest’ – only ‘at rest’ relative to other bodies). Isaac Newton knew that it was impossible to measure something’s absolute velocity (rather than velocity relative to an observer), but still, like Descartes, supported the idea that there was an absolute space and absolute velocity – we just couldn’t measure it.
Lets skip ahead some centuries. The year is 1893; the U.S. Supreme Court declared the tomato was a vegetable, Gandhi campaigned against segregation in South Africa, and the U.S. railroad industry bubble had just popped, forcing the government to bail out AIG for $85 billion. Or something. Also, by this point, most scientists thought light traveled in waves. Given that in order for something to travel in a wave, something has to be waving, scientists posited there was this luminiferous ether that pervaded the universe, allowing light to travel between stars and candles and those fish with the crazy headlights. It makes perfect sense. In order for sound waves to travel, they need air to travel through; in order for light waves to travel, they need the ether.
Ernst Mach, A philosopher read by many contemporaries (including Einstein), said that Newton and Descartes were wrong: absolute space and absolute motion are meaningless. It’s all relative, and only relative motion has any meaning. It is both physically impossible to measure the an objects “real” velocity, and also philosophically nonsensical. The ether, however, was useful. According to Mach and others, we could still measure something kind of like absolute position and velocity by measuring things in relationship to that all-pervasive ether. Presumably, the ether was just sitting still, doing whatever ether does, so we could use its stillness as a reference point and measure how fast things were going relative to it.
Well, in theory. Earth is hurtling through space, orbiting the sun at about 70,000 miles per hour, right? And it’s spinning too, at about a thousand miles an hour. But the ether is staying still. And light, supposedly, always travels at the same speed through the ether no matter what. So in theory, light should look like it’s moving a bit faster if we’re moving toward its source, relative to the ether, and a bit slower, if we’re moving away from it, relative to the ether. It’s just like if you’re in a train hurdling toward a baseball pitcher at 100 mph, and the pitcher throws a ball at you, also at 100 mph, in a futile attempt to stop the train. To you, the baseball will look like it’s going twice as fast, because you’re moving toward it.
It turns out measuring the speed of light in relation to the ether was really difficult. A bunch of very clever people made a bunch of very clever instruments which really should have measured the speed of earth moving through the ether, based on small observed differences of the speed of light going in different directions, but the experiments always showed light moving at the same speed. Scientists figured this must mean the earth was actually exerting a pull on the ether in its vicinity, dragging it along with it as the earth hurtled through space, explaining why light seemed to be constant in both directions when measured on earth. They devised even cleverer experiments that would account for such an ether drag, but even those seemed to come up blank. Their instruments, it was decided, simply were not yet fine-tuned enough to measure such small variations in the speed of light.
The earth moving through the ether. [via]
Not so fast! shouted Lorentz, except he shouted it in Dutch. Lorentz used the new electromagnetic theory to suggest that the null results of the ether experiments were actually a result, not of the earth dragging the ether along behind it, but of physical objects compressing when they moved against the ether. The experiments weren’t showing any difference in the speed of light they sought because the measuring instruments themselves contracted to just the right length to perfectly offset the difference in the velocity of light, when measuring “into” the ether. The ether was literally squeezing the electrons in the meter stick together so it became a little shorter; short enough to inaccurately measure light’s speed. The set of equations used to describe this effect became known as Lorentz Transformations. One property of these transformations was that the physical contractions would, obviously, appear the same from any observer. No matter how fast you were going relative to your measuring device, if it were moving into the ether, you would see it contracting slightly to accommodate the measurement difference.
Not so fast! shouted Poincaré, except he shouted it in French. This property of transformations to always appear the same, relative to the ether, was actually a problem. Remember that 500 years of physics that said there is no way to mechanically determine your absolute speed or absolute location in space? Yeah, so did Poincaré. He said the only way you could measure velocity or location was matter-to-matter, not matter-to-ether, so the Lorentz transformations didn’t fly.
It’s worth taking a brief aside to talk about the underpinnings of the theories of both Lorentz and Poincaré. Their theories were based on experimental evidence, which is to say, they based their reasoning on contraction on apparent experimental evidence of said contraction, and they based their theories of relativity off of experimental evidence of motion being relative.
Einstein and Relativity
When Einstein hit the scene in 1905, he approached relativity a bit differently. Instead of trying to fit the apparent contraction of objects from the ether drift experiment to a particular theory, Einstein began with the assumption that light always appeared to move at the same rate, regardless of the relative velocity of the observer. The other assumption he began with was that there was no privileged frame of reference; no absolute space or velocity, only the movement of matter relative to other matter. I’ll work out the math later, but, unsurprisingly, it turned out that working out these assumptions led to exactly the same transformation equations as Lorentz came up with experimentally.
The math was the same. The difference was in the interpretation of the math. Einstein’s theory required no ether, but what’s more, it did not require any physical explanations at all. Because Einstein’s theory of special relativity rested on two postulates about measurement, the theory’s entire implications rested in its ability to affect how we measure or observe the universe. Thus, the interpretation of objects “contracting,” under Einstein’s theory, was that they were not contracting at all. Instead, objects merely appear as though they contract relative to the movement of the observer. Another result of these transformation equations is that, from the perspective of the observer, time appears to move slower or faster depending on the relative speed of what is being observed. Lorentz’s theory predicted the same time dilation effects, but he just chalked it up to a weird result of the math that didn’t actually manifest itself. In Einstein’s theory, however, weird temporal stretching effects were Actually What Was Going On.
To reiterate: the math of Lorentz, Einstein, and Poincaré were (at least at this early stage) essentially equivalent. The result was that no experimental result could favor one theory over another. The observational predictions between each theory were exactly the same.
Relativity’s Supporters in America
I’m focusing on America here because it’s rarely focused on in the historiography, and it’s about time someone did. If I were being scholarly and citing my sources, this might actually be a novel contribution to historiography. Oh well, BLOG! All my primary sources are in that Zotero library I linked to earlier.
In 1910, Daniel Comstock wrote a popular account of the relativity of Lorentz and Einstein, to some extent conflating the two. He suggested that if Einstein’s postulates could be experimentally verified, his special theory of relativity would be true. “If either of these postulates be proved false in the future, then the structure erected can not be true in is present form. The question is, therefore, an experimental one.” Comstock’s statement betrays a misunderstanding of Einstein’s theory, though, because, at the time of that writing, there was no experimental difference between the two theories.
Gilbert Lewis and Richard Tolman presented a paper at the 1908 American Physical Society in New York, where they describe themselves as fully behind Einstein over Lorentz. Oddly, the consider Einstein’s theory to be correct, as opposed to Lorentz’s, because his postulates were “established on a pretty firm basis of experimental fact.” Which, to reiterate, couldn’t possibly have been a difference between Lorentz and Einstein. Even more oddly still, they presented the theory not as one of physics or of measurement, but of psychology (a bit like 14th century Oresme). The two went on to separately write a few articles which supposedly experimentally confirmed the postulates of special relativity.
In fact, the few Americans who did seem to engage with the actual differences between Lorentz and Einstein did so primarily in critique. Louis More, a well-respected physicist from Cincinnati, labeled the difference as metaphysical and primarily useless. This American critique was fairly standard.
At the 1909 America Physical Society meeting in Boston, one physicist (Harold Wilson) claimed his experiments showed the difference between Einstein and Lorentz. One of the few American truly theoretical physicists, W.S. Franklin, was in attendance, and the lectures he saw inspired him to write a popular account of relativity in 1911; in it, he found no theoretical difference between Lorentz and Einstein. He tended to side theoretically with Einstein, but assumed Lorentz’s theory implied the same space and time dilation effects, which they did not.
Even this series of misunderstandings should be taken as shining examples in the context of an American approach to theoretical physics that was largely antagonistic, at times decrying theoretical differences entirely. At a symposium on Ether Theories at the 1911 APS, the presidential address by William Magie was largely about the uselessness of relativity because, according to him, physics should be a functional activity based in utility and experimentation. Joining Magie’s “side” in the debate were Michelson, Morley, and Arthur Gordon Webster, the co-founder of the America Physical Society. Of those at the meeting supporting relativity, Lewis was still convinced Einstein differed experimentally from Lorentz, and Franklin and Comstock each felt there was no substantive difference between the two. In 1912, Indiana University’s R.D. Carmichael stated Einstein’s postulates were “a direct generalization from experiment.” In short, the American’s were really focused on experiment.
Of Einstein’s theory, Louis More wrote in 1912:
Professor Einstein’s theory of Relativity [… is] proclaimed somewhat noisily to be the greatest revolution in scientific method since the time of Newton. That [it is] revolutionary there can be no doubt, in so far as [it] substitutes mathematical symbols as the basis of science and denies that any concrete experience underlies these symbols, thus replacing an objective by a subjective universe. The question remains whether this is a step forward or backward […] if there is here any revolution in thought, it is in reality a return to the scholastic methods of the Middle Ages.
More goes on to say how the “Anglo-Saxons” demand practical results, not the unfathomable theories of “the German mind.” Really, that quote about sums it up. By this point, the only Americans who even talked about relativity were the ones who trained in Germany.
I’ll end here, where most histories of the reception of relativity begin: the first Solvay Conference. It’s where this beautiful picture was taken.
To sum up: in the seven year’s following Einstein’s publication, the only Americans who agreed with Einstein were ones who didn’t quite understand him. You, however, will understand it much better, if you only read the next post [coming this week!].
First Solvay Conference. [via]
11 02 2014
[edit: I'm realizing I didn't make it clear in this post that I'm aware many historians consider themselves scientists, and that there's plenty of scientific historical archaeology and anthropology. That's exactly what I'm advocating there be more of, and more varied.]
Short Answer: Yes.
Less Snarky Answer: Historians need to be flexible to fresh methods, fresh perspectives, and fresh blood. Maybe not that last one, I guess, as it might invite vampires.Okay, I suppose this answer wasn’t actually less snarky.
The long answer is that historians don’t necessarily need scientists, but that we do need fresh scientific methods. Perhaps as an accident of our association with the ill-defined “humanities”, or as a result of our being placed in an entirely different culture (see: C.P. Snow), most historians seem fairly content with methods rooted in thinking about text and other archival evidence. This isn’t true of all historians, of course – there are economic historians who use statistics, historians of science who recreate old scientific experiments, classical historians who augment their research with archaeological findings, archival historians who use advanced ink analysis, and so forth. But it wouldn’t be stretching the truth to say that, for the most part, historiography is the practice of thinking cleverly about words to make more words.
I’ll argue here that our reliance on traditional methods (or maybe more accurately, our odd habit of rarely discussing method) is crippling historiography, and is making it increasingly likely that the most interesting and innovative historical work will come from non-historians. Sometimes these studies are ill-informed, especially when the authors decide not to collaborate with historians who know the subject, but to claim that a few ignorant claims about history negate the impact of these new insights is an exercise in pedantry.
In defending the humanities, we like to say that scientists and technologists with liberal arts backgrounds are more well-rounded, better citizens of the world, more able to contextualize their work. Non-humanists benefit from a liberal arts education in pretty much all the ways that are impossible to quantify (and thus, extremely difficult to defend against budget cuts). We argue this in the interest of rounding a person’s knowledge, to make them aware of their past, of their place in a society with staggering power imbalances and systemic biases.
Humanities departments should take a page from their own books. Sure, a few general ed requirements force some basic science and math… but I got an undergraduate history degree in a nice university, and I’m well aware how little STEM I actually needed to get through it. Our departments are just as guilty of narrowness as those of our STEM colleagues, and often because of it, we rely on applied mathematicians, statistical physicists, chemists, or computer scientists to do our innovative work for (or sometimes, thankfully, with) us.
Of course, there’s still lots of innovative work to be done from a textual perspective. I’m not downplaying that. Not everyone needs to use crazy physics/chemistry/computer science/etc. methods. But there’s a lot of low hanging fruit at the intersection of historiography and the natural sciences, and we’re not doing a great job of plucking it.
The story below is illustrative.
Last night, Blaise Agüera y Arcas presented his research on Gutenberg to a packed house at our rare books library. He’s responsible for a lot of the cool things that have come out of Microsoft in the last few years, and just got a job at Google, where presumably he will continue to make cool things. Blaise has degrees in physics and applied mathematics. And, a decade ago, Blaise and historian/librarian Paul Needham sent ripples through the History of the Book community by showing that Gutenberg’s press did not work at all the way people expected.
It was generally assumed that Gutenberg employed a method called punchcutting in order to create a standard font. A letter carved into a metal rod (a “punch”) would be driven into a softer metal (a “matrix”) in order to create a mold. The mold would be filled with liquid metal which hardened to form a small block of a single letter (a “type”), which would then be loaded onto the press next to other letters, inked, and then impressed onto a page. Because the mold was metal, many duplicate “types” could be made of the same letter, thus allowing many uses of the same letter to appear identical on a single pressed page.
Punch matrix system. [via]
This process is what allowed all the duplicate letters to appear identical in Gutenberg’s published books. Except, of course, careful historians of early print noticed that letters weren’t, in fact, identical. In the 1980s, Paul Needham and a colleague attempted to produce an inventory of all the different versions of letters Gutenberg used, but they stopped after frequently finding 10 or more obviously distinct versions of the same letter.
Type to be pressed. [via]
This was perplexing, but the subject was bracketed away for a while, until Blaise Agüera y Arcas came to Princeton and decided to work with Needham on the problem. Using extremely high-resolution imagining techniques, Blaise noted that there were in fact hundreds of versions of every letter. Not only that, there were actually variations and regularities in the smaller elements that made up letters. For example, an “n” was formed by two adjacent vertical lines, but occasionally the two vertical lines seem to have flipped places entirely. The extremely basic letter “i” itself had many variations, but within those variations, many odd self-similarities.
Needham’s inventory of Gutenberg type. [via]
Historians had, until this analysis, assumed most letter variations were due to wear of the type blocks. This analysis blew that hypothesis out of the water. These “i”s were clearly not all made in the same mold; but then, how had they been made? To answer this, they looked even closer at the individual letters.
Variations in the letter “i” in Gutenberg’s type. [via]
It’s difficult to see at first glance, but they found something a bit surprising. The letters appeared to be formed of overlapping smaller parts: a vertical line, a diagonal box, and so forth. The below figure shows a good example of this. The glyphs on the bottom have have a stem dipping below the bottom horizontal line, while the glyphs at the top do not.
Close up of Gutenberg letters, with light shining through page. [via]
The conclusion Needham and Agüera y Arcas drew, eventually, was that the punchcutting method must not have been used for Gutenberg’s early material. Instead, a set of carved “strokes” were pushed into hard sand or soft clay, configured such that the strokes would align to form various letters, not unlike the formation of cuneiform. This mold would then be used to cast letters, creating the blocks we recognize from movable type. The catch is that this soft clay could only cast letters a few times before it became unusable and would need to be recreated. As Gutenberg needed multiple instances of individual letters per page, many of those letters would be cast from slightly different soft molds.
Abbreviation of ‘per’. [via]
At the end of his talk, Blaise made an offhand comment: how is it that historians/bibliographers/librarians have been looking at these Gutenbergs for so long, discussing the triumph of their identical characters, and not noticed that the characters are anything but uniform? Or, of those who had noticed it, why hadn’t they raised any red flags?
The insights they produced weren’t staggering feats of technology. He used a nice camera, a light shining through the pages of an old manuscript, and a few simple image recognition and clustering algorithms. The clustering part could even have been done by hand, and actually had been, by Paul Needham. And yes, it’s true, everything is obvious in hindsight, but there were a lot of eyes on these bibles, and odds are if some of them had been historians who were trained in these techniques, this insight could have come sooner. Every year students do final projects and theses and dissertations, but what percent of those use techniques from outside historiography?
In short, there’s a lot of very basic assumptions we make about the past that could probably be updated significantly if we had the right skillset, or knew how to collaborate with those who did. I think people like William Newman, who performs Newton’s alchemical experiments, is on the right track. As is Shawn Graham, who reanimates the trade networks of ancient Rome using agent-based simulations, or Devon Elliott, who creates computational and physical models of objects from the history of stage magic. Elliott’s models have shown that certain magic tricks couldn’t possibly have worked as they were described to.
The challenge is how to encourage this willingness to reach outside traditional historiographic methods to learn about the past. Changing curricula to be more flexible is one way, but that is a slow and institutionally difficult process. Perhaps faculty could assign group projects to students taking their gen-ed history courses, encouraging disciplinary mixes and non-traditional methods. It’s an open question, and not an easy one, but it’s one we need to tackle.
4 02 2014
Operationalize: to express or define (something) in terms of the operations used to determine or prove it.
Precision deceives. Quantification projects an illusion of certainty and solidity no matter the provenance of the underlying data. It is a black box, through which uncertain estimations become sterile observations. The process involves several steps: a cookie cutter to make sure the data are all shaped the same way, an equation to aggregate the inherently unique, a visualization to display exact values from a process that was anything but.
In this post, I suggest that Moretti’s discussion of operationalization leaves out an integral discussion on precision, and I introduce a new term, appreciability, as a constraint on both accuracy and precision in the humanities. This conceptual constraint paves the way for an experimental digital humanities.
Operationalizing and the Natural Sciences
An operationalization is the use of definition and measurement to create meaningful data. It is an incredibly important aspect of quantitative research, and it has served the western world well for at leas 400 years. Franco Moretti recently published a LitLab Pamphlet and a nearly identical article in the New Left Review about operationalization, focusing on how it can bridge theory and text in literary theory. Interestingly, his description blurs the line between the operationalization of his variables (what shape he makes the cookie cutters that he takes to his text) and the operationalization of his theories (how the variables interact to form a proxy for his theory).
Moretti’s account anchors the practice in its scientific origin, citing primarily physicists and historians of physics. This is a deft move, but an unexpected one in a recent DH environment which attempts to distance itself from a narrative of humanists just playing with scientists’ toys. Johanna Drucker, for example, commented on such practices:
[H]umanists have adopted many applications [...] that were developed in other disciplines. But, I will argue, such [...] tools are a kind of intellectual Trojan horse, a vehicle through which assumptions about what constitutes information swarm with potent force. These assumptions are cloaked in a rhetoric taken wholesale from the techniques of the empirical sciences that conceals their epistemological biases under a guise of familiarity.
Rendering observation (the act of creating a statistical, empirical, or subjective account or image) as if it were the same as the phenomena observed collapses the critical distance between the phenomenal world and its interpretation, undoing the basis of interpretation on which humanistic knowledge production is based.
But what Drucker does not acknowledge here is that this positivist account is a century-old caricature of the fundamental assumptions of the sciences. Moretti’s account of operationalization as it percolates through physics is evidence of this. The operational view very much agrees with Drucker’s thesis, where the phenomena observed takes second stage to a definition steeped in the nature of measurement itself. Indeed, Einstein’s introduction of relativity relied on an understanding that our physical laws and observations of them rely not on the things themselves, but on our ability to measure them in various circumstances. The prevailing theory of the universe on a large scale is a theory of measurement, not of matter. Moretti’s reliance on natural scientific roots, then, is not antithetical to his humanistic goals.
I’m a bit horrified to see myself typing this, but I believe Moretti doesn’t go far enough in appropriating natural scientific conceptual frameworks. When describing what formal operationalization brings to the table that was not there before, he lists precision as the primary addition. “It’s new because it’s precise,” Moretti claims, “Phaedra is allocated 29 percent of the word-space, not 25, or 39.” But he asks himself: is this precision useful? Sometimes, he concludes, “It adds detail, but it doesn’t change what we already knew.”
From Moretti, ‘Operationalizing’, New Left Review.
I believe Moretti is asking the wrong first question here, and he’s asking it because he does not steal enough from the natural sciences. The question, instead, should be: is this precision meaningful? Only after we’ve assessed the reliability of new-found precision can we understand its utility, and here we can take some inspiration from the scientists, in their notions of accuracy, precision, uncertainty, and significant figures.
First some definitions. The accuracy of a measurement is how close it is to the true value you are trying to capture, whereas the precision of a measurement is how often a repeated measurement produces the same results. The number of significant figures is a measurement of how precise the measuring instrument can possibly be. False precision is the illusion that one’s measurement is more precise than is warranted given the significant figures. Propagation of uncertainty is the pesky habit of false precision to weasel its way into the conclusion of a study, suggesting conclusions that might be unwarranted.
Accuracy roughly corresponds to how well-suited your operationalization is to finding the answer you’re looking for. For example, if you’re interested in the importance of Gulliver in Gulliver’s Travels, and your measurement is based on how often the character name is mentioned (12 times, by the way), you can be reasonably certain your measurement is inaccurate for your purposes.
Accuracy and Precision. [via]
Precision roughly corresponds to how fine-tuned your operationalization is, and how likely it is that slight changes in measurement will affect the outcomes of the measurement. For example, if you’re attempting to produce a network of interacting characters from The Three Musketeers, and your measuring “instrument” is increase the strength of connection between two characters every time they appear in the same 100-word block, then you might be subject to difficulties of precision. That is, your network might look different if you start your sliding 100-word window from the 1st word, the 15th word, or the 50th word. The amount of variation in the resulting network is the degree of imprecision of your operationalization.
Significant figures are a bit tricky to port to DH use. When you’re sitting at home, measuring some space for a new couch, you may find that your meter stick only has tick marks to the centimeter, but nothing smaller. This is your highest threshold for precision; if you eyeballed and guessed your space was actually 250.5cm, you’ll have reported a falsely precise number. Others looking at your measurement may have assumed your meter stick was more fine-grained than it was, and any calculations you make from that number will propagate that falsely precise number.
Uncertainty propagation is especially tricky when you wind up combing two measurements together, when one is more precise and the other less. The rule of thumb is that your results can only be as precise as the least precise measurements that made its way into your equation. The final reported number is then generally in the form of 250 (±1 cm). Thankfully, for our couch, the difference of a centimeter isn’t particularly appreciable. In DH research, I have rarely seen any form of precision calculated, and I believe some of those projects would have reported different results had they accurately represented their significant figures.
Significant Figures. [via]
Precision, Accuracy, and Appreciability in DH
Moretti’s discussion of the increase of precision granted by operationalization leaves out any discussion of the certainty of that precision. Let’s assume for a moment that his operationalization is accurate (that is, his measurement is a perfect conversion between data and theory). Are his measurements precise? In the case of Phaedra, the answer at first glance is yes, words-per-character in a play would be pretty robust against slight changes in the measurement process.
And yet, I imagine, that answer will probably not sit well with some humanists. They may ask themselves: Is Oenone’s 12% appreciably different from Theseus’s 13% of the word-space of the play? In the eyes of the author? Of the actors? Of the audience? Does the difference make a difference?
The mechanisms by which people produce and consume literature is not precise. Surely Jean Racine did not sit down intending to give Theseus a fraction more words than Oenone. Perhaps in DH we need a measurement of precision, not of the measuring device, but of our ability to interact with the object we are studying. In a sense, I’m arguing, we are not limited to the precision of the ruler when measuring humanities objects, but to the precision of the human.
In the natural sciences, accuracy is constrained by precision: you can only have as accurate a measurement as your measuring device is precise. In the corners of humanities where we study how people interact with each other and with cultural objects, we need a new measurement that constrains both precision and accuracy: appreciability. A humanities quantification can only be as precise as that precision is appreciable by the people who interact with matter at hand. If two characters differ by a single percent of the wordspace, and that difference is impossible to register in a conscious or subconscious level, what is the meaning of additional levels of precision (and, consequently, additional levels of accuracy)?
Experimental Digital Humanities
Which brings us to experimental DH. How does one evaluate the appreciability of an operationalization except by devising clever experiments to test the extent of granularity a person can register? Without such understanding, we will continue to create formulae and visualizations which portray a false sense of precision. Without visual cues to suggest uncertainty, graphs present a world that is exact and whose small differentiations appear meaningful or deliberate.
Experimental DH is not without precedent. In Reading Tea Leaves (Chang et al., 2009), for example, the authors assessed the quality of certain topic modeling tweaks based on how a large number of people assessed the coherence of certain topics. If this approach were to catch on, as well as more careful acknowledgements of accuracy, precision, and appreciability, then those of us who are making claims to knowledge in DH can seriously bolster our cases.
There are some who present the formal nature of DH as antithetical to the highly contingent and interpretative nature of the larger humanities. I believe appreciability and experimentation can go some way alleviating the tension between the two schools, building one into the other. On the way, it might build some trust in humanists who think we sacrifice experience for certainty, and in natural scientists who are skeptical of our abilities to apply quantitative methods.
Right now, DH seems to find its most fruitful collaborations in computer science or statistics departments. Experimental DH would open the doors to new types of collaborations, especially with psychologists and sociologists.
I’m at an extremely early stage in developing these ideas, and would welcome all comments (especially those along the lines of “You dolt! Appreciability already exists, we call it x.”) Let’s see where this goes.
18 01 2014
There’s an oft-spoken and somewhat strawman tale of how the digital humanities is bridging C.P. Snow’s “Two Culture” divide, between the sciences and the humanities. This story is sometimes true (it’s fun putting together Ocean’s Eleven-esque teams comprising every discipline needed to get the job done) and sometimes false (plenty of people on either side still view the other with skepticism), but as a historian of science, I don’t find the divide all that interesting. As Snow’s title suggests, this divide is first and foremost cultural. There’s another overlapping divide, a bit more epistemological, methodological, and ontological, which I’ll explore here. It’s the nomothetic(type)/idiographic(token) divide, and I’ll argue here that not only are its barriers falling, but also that the distinction itself is becoming less relevant.
Nomothetic (Greek for “establishing general laws”-ish) and Idiographic (Greek for “pertaining to the individual thing”-ish) approaches to knowledge have often split the sciences and the humanities. I’ll offload the hard work onto Wikipedia:
Nomothetic is based on what Kant described as a tendency to generalize, and is typical for the natural sciences. It describes the effort to derive laws that explain objective phenomena in general.
Idiographic is based on what Kant described as a tendency to specify, and is typical for the humanities. It describes the effort to understand the meaning of contingent, unique, and often subjective phenomena.
These words are long and annoying to keep retyping, and so in the longstanding humanistic tradition of using new words for words which already exist, henceforth I shall refer to nomothetic as type and idiographic as token. I use these because a lot of my digital humanities readers will be familiar with their use in text mining. If you counted the number of unique words in a text, you’d be be counting the number of types. If you counted the number of total words in a text, you’d be counting the number of tokens, because each token (word) is an individual instance of a type. You can think of a type as the platonic ideal of the word (notice the word typical?), floating out there in the ether, and every time it’s actually used, it’s one specific token of that general type.
The Token/Type Distinction
Usually the natural and social sciences look for general principles or causal laws, of which the phenomena they observe are specific instances. A social scientist might note that every time a student buys a $500 textbook, they actively seek a publisher to punch, but when they purchase $20 textbooks, no such punching occurs. This leads to the discovery of a new law linking student violence with textbook prices. It’s worth noting that these laws can and often are nuanced and carefully crafted, with an awareness that they are neither wholly deterministic nor ironclad.
The humanities (or at least history, which I’m more familiar with) are more interested in what happened than in what tends to happen. Without a doubt there are general theories involved, just as in the social sciences there are specific instances, but the intent is most-often to flesh out details and create a particular internally consistent narrative. They look for tokens where the social scientists look for types. Another way to look at it is that the humanist wants to know what makes a thing unique, and the social scientist wants to know what makes a thing comparable.
It’s been noted these are fundamentally different goals. Indeed, how can you in the same research articulate the subjective contingency of an event while simultaneously using it to formulate some general law, applicable in all such cases? Rather than answer that question, it’s worth taking time to survey some recent research.
A recent digital humanities panel at MLA elicited responses by Ted Underwood and Haun Saussy, of which this post is in part itself a response. One of the papers at the panel, by Long and So, explored the extent to which haiku-esque poetry preceded what is commonly considered the beginning of haiku in America by about 20 years. They do this by teaching the computer the form of the haiku, and having it algorithmically explore earlier poetry looking for similarities. Saussy comments on this work:
[...] macroanalysis leads us to reconceive one of our founding distinctions, that between the individual work and the generality to which it belongs, the nation, context, period or movement. We differentiate ourselves from our social-science colleagues in that we are primarily interested in individual cases, not general trends. But given enough data, the individual appears as a correlation among multiple generalities.
One of the significant difficulties faced by digital humanists, and a driving force behind critics like Johanna Drucker, is the fundamental opposition between the traditional humanistic value of stressing subjectivity, uniqueness, and contingency, and the formal computational necessity of filling a database with hard decisions. A database, after all, requires you to make a series of binary choices in well-defined categories: is it or isn’t it an example of haiku? Is the author a man or a woman? Is there an author or isn’t there an author?
Underwood addresses this difficulty in his response:
Though we aspire to subtlety, in practice it’s hard to move from individual instances to groups without constructing something like the sovereign in the frontispiece for Hobbes’ Leviathan – a homogenous collection of instances composing a giant body with clear edges.
But he goes on to suggest that the initial constraint of the digital media may not be as difficult to overcome as it appears. Computers may even offer us a way to move beyond the categories we humanists use, like genre or period.
Aren’t computers all about “binary logic”? If I tell my computer that this poem both is and is not a haiku, won’t it probably start to sputter and emit smoke?
Well, maybe not. And actually I think this is a point that should be obvious but just happens to fall in a cultural blind spot right now. The whole point of quantification is to get beyond binary categories — to grapple with questions of degree that aren’t well-represented as yes-or-no questions. Classification algorithms, for instance, are actually very good at shades of gray; they can express predictions as degrees of probability and assign the same text different degrees of membership in as many overlapping categories as you like.
Here we begin to see how the questions asked of digital humanists (on the one side; computational social scientists are tackling these same problems) are forcing us to reconsider the divide between the general and the specific, as well as the meanings of categories and typologies we have traditionally taken for granted. However, this does not yet cut across the token/type divide: this has gotten us to the macro scale, but it does not address general principles or laws that might govern specific instances. Historical laws are a murky subject, prone to inducing fits of anti-deterministic rage. Complex Systems Science and the lessons we learn from Agent-Based Modeling, I think, offer us a way past that dilemma, but more on that later.
For now, let’s talk about influence. Or diffusion. Or intertextuality. Matthew Jockers has been exploring these concepts, most recently in his book Macroanalysis. The undercurrent of his research (I think I’ve heard him call it his “dangerous idea”) is a thread of almost-determinism. It is the simple idea that an author’s environment influences her writing in profound and easy to measure ways. On its surface it seems fairly innocuous, but it’s tied into a decades-long argument about the role of choice, subjectivity, creativity, contingency, and determinism. One word that people have used to get around the debate is affordances, and it’s as good a word as any to invoke here. What Jockers has found is a set of environmental conditions which afford certain writing styles and subject matters to an author. It’s not that authors are predetermined to write certain things at certain times, but that a series of factors combine to make the conditions ripe for certain writing styles, genres, etc., and not for others. The history of science analog would be the idea that, had Einstein never existed, relativity and quantum physics would still have come about; perhaps not as quickly, and perhaps not from the same person or in the same form, but they were ideas whose time had come. The environment was primed for their eventual existence.
It is here we see the digital humanities battling with the token/type distinction, and finding that distinction less relevant to its self-identification. It is no longer a question of whether one can impose or generalize laws on specific instances, because the axes of interest have changed. More and more, especially under the influence of new macroanalytic methodologies, we find that the specific and the general contextualize and augment each other.
An example of shape affording certain actions by constraining possibilities and influencing people. [via]
The computational social sciences are converging on a similar shift. Jon Kleinberg likes to compare some old work by Stanley Milgram , where he had people draw maps of cities from memory, with digital city reconstruction projects which attempt to bridge the subjective and objective experiences of cities. The result in both cases is an attempt at something new: not quite objective, not quite subjective, and not quite intersubjective. It is a representation of collective individual experiences which in its whole has meaning, but also can be used to contextualize the specific. That these types of observations can often lead to shockingly accurate predictive “laws” isn’t really the point; they’re accidental results of an attempt to understand unique and contingent experiences at a grand scale.
It is no surprise that the token/type divide is woven into the subjective/objective divide. However, as Daston and Galison have pointed out, objectivity is not an ahistorical category. It has a history, is only positively defined in relation to subjectivity, and neither were particularly useful concepts before the 19th century.
Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow are uncertain. [via Eric Fischer]
I would argue, as well, that the nomothetic and idiographic divide is one which is outliving its historical usefulness. Work from both the digital humanities and the computational social sciences is converging to a point where the objective and the subjective can peaceably coexist, where contingent experiences can be placed alongside general predictive principles without any cognitive dissonance, under a framework that allows both deterministic and creative elements. It is not that purely nomothetic or purely idiographic research will no longer exist, but that they no longer represent a binary category which can usefully differentiate research agendas. We still have Snow’s primary cultural distinctions, of course, and a bevy of disciplinary differences, but it will be interesting to see where this shift in axes takes us.
8 12 2013
Hah! I tricked you. I don’t intend to define digital humanities here—too much blood has already been spilled over that subject. I’m sure we all remember the terrible digital humanities / humanities computing wars of 2004, now commemorated yearly under a Big Tent in the U.S., Europe, or in 2015, Australia. Most of us still suffer from ACH or
ALLC (edit: I’ve been reminded the more politically correct acronym these days is EADH).
Instead, I’m here to report the findings of an extremely informal survey, with a sample size of 5, inspired by Paige Morgan’s question of what courses an undergraduate interested in digital humanities should take:
The question inspired a long discussion, worth reading through if you’re interested in digital humanities curricula. I suggested, were the undergrad interested in the heavily computational humanities (like Ted Underwood, Ben Schmidt, etc.), they might take linear algebra, statistics for social science, programming 1 & 2, web development, and a social science (like psych) research methods course, along with all their regular humanities courses. Others suggested to remove some and include others, and of course all of these are pipe dreams unless our mystery undergrad is in the six year program.
The discussion got me thinking: how did the digital humanists we know and love get to where they are today? Given that the basic ethos of DH is that if you want to know something, you just have to ask, I decided to ask a few well-respected DHers how someone might go about reaching expertise in their subject matter. This isn’t a question of how to define digital humanities, but of the sorts of things the digital humanists we know and love learned to get where they are today. I asked:
The Pipe Dream Curriculum. [via]
Some of you may have seen this tweet by Paige Morgan this morning, asking about what classes an undergraduate student should take hoping to pursue DH. I’ve emailed you, a random and diverse smattering of highly recognizable names associated with DH, in the hopes of getting a broader answer than we were able to generate through twitter alone.
I know you’re all extremely busy, so please excuse my unsolicited semi-mass email and no worries if you don’t get around to replying.
If you do reply, however, I’d love to get a list of undergraduate courses (traditional humanities or otherwise) that you believe was or would be instrumental to the research you do. My list, for example, would include historical methods, philosophy of science, linear algebra, statistics, programming, and web development. I’ll take the list of lists and write up a short blog post about them, because I believe it would be beneficial for many new students who are interested in pursuing DH in all its guises. I’d also welcome suggestions for other people and “schools of DH” I’m sure to have missed.
And because the people in DH are awesome and forthcoming, I got many replies back. I’ll list them first here, and then attempt some preliminary synthesis below.
The first reply was from Ted Underwood, who was afraid my question skirted a bit too close to defining DH, saying:
No matter how heavily I hedge and qualify my response (“this is just a personal list relevant to the particular kind of research I do …”), people will tend to read lists like this as tacit/covert/latent efforts to DEFINE DH — an enterprise from which I never harvest anything but thorns.
Thankfully he came back to me a bit later, saying he’d worked up the nerve to reply to my survey because he’s “coming to the conclusion that this is a vital question we can’t afford to duck, even if it’s controversial [emphasis added]“. Ted continued:
So here goes, with three provisos:
- I’m talking only about my own field (literary text mining), and not about the larger entity called “DH,” which may be too deeply diverse to fit into a single curriculum.
- A lot of this is not stuff I actually took in the classroom.
- I really don’t have strong opinions about how much of this should be taken as an undergrad, and what can wait for grad school. In practice, no undergrad is going to prepare themselves specifically for literary text mining (at least, I hope not). They should be aiming at some broader target.
But at some point, as preparation for literary text-mining, I’d recommend
- A lot of courses in literary history and critical theory (you probably need a major’s worth of courses in some aspect of literary studies).
- At least one semester of experience programming. Two semesters is better. But existing CS courses may not be the most efficient delivery system. You probably don’t need big-O notation. You do need data structures. You may not need to sweat the fine points of encapsulation. You probably do need to know about version control. I think there’s room for a “Programming for Humanists” course here.
- Maybe one semester of linguistics (I took historical linguistics, but corpus linguistics would also work).
- Statistics — a methods course for social scientists would be great.
- At least one course in data mining / machine learning. This may presuppose more math than one semester of statistics will provide, so
- Your recommendation of linear algebra is probably also a good idea.
I doubt all of that will fit in anyone’s undergrad degree. So in practice, any undergrad with courses in literary history plus a semester or two of programming experience, and perhaps statistics, would be doing very well.
So Underwood’s reply was that focusing too much in undergrad is not necessarily ideal, but were an undergraduate interested in literary text mining, they wouldn’t go astray with literary history, critical theory, a programming for humanists course, linguistics, statistics, data mining, and potentially linear algebra.
While Underwood is pretty well known for his computational literary history, Johanna Drucker is probably most well known in our circles for her work in DH criticism. Her reply was concise and helpful:
Look at http://dh101.humanities.ucla.edu
In the best of all possible worlds, this would be followed by specialized classes in database design, scripting for the humanities, GIS/mapping, virtual worlds design, metadata/classification/culture, XML/markup, and data mining (textual corpora, image data mining, network analysis), and complex systems modeling, as well as upper division courses in disciplines (close/distant reading for literary studies, historical methods and mapping etc.).
The site she points is an online coursebook that provides a broad overview of DH concepts, along with exercises and tutorials, that would make a good basic course on the groundwork of DH. She then lists a familiar list of computer-related and humanities course that might be useful.
The next reply came from Melissa Terras, the director of the DH center (I’m sorry, centre) at UCL. Her response was a bit more general:
My first response is that they must be interested in Humanities research – and make the transition to being taught about Humanities, to doing research in the Humanities, and get the bug for finding out new information about a Humanities topic. It doesn’t matter what the Humanities subject is – but they must understand Humanities research questions, and what it means to undertake new research in the Humanities proper. (Doesn’t matter if their research project has no computing component, it’s about a hunger for new knowledge in this area, rather than digesting prior knowledge).
Like Underwood and Drucker, Terras is stressing that students cannot forget the humanities for the digital.
Then they must become information literate, and IT literate. We have a variety of training courses at our institution, and there is also the “European Driving License in IT” which is basic IT skills. They must get the bug for learning more about computing too. They’ll know after some basic courses whether they are a natural fit to computing.
Without the bug to do research, and the bug to understand more about computing, they are sunk for pursuing DH. These are the two main prerequisites.
Interestingly (but not surprisingly, given general DH trends), Terras frames passion about computing as more important than any particular skill.
Once they get the bug, then taking whatever courses are on offer to them at their institution – either for credit modules, or pure training courses in various IT methods, would stand them in good stead. For example, you are not going to get a degree course in Photoshop, but attending 6 hours of training in that…. plus spreadsheets, plus databases, plus XML, plus web design, would prepare you for pursuing a variety of other courses. Even if the institution doesnt offer taught DH courses, chances are they offer training in IT. They need to get their hands dirty, and to love learning more about computing, and the information environment we inhabit.
Her stress on hyper-focused courses of a few hours each is also interesting, and very much in line with our “workshop and summer school”-focused training mindset in DH.
It’s at that stage I’d be looking for a master’s program in DH, to take the learning of both IT and the humanities to a different level. Your list excludes people who have done “pure” humanities as an undergrad to pursuing DH, and actually, I think DH needs people who are, ya know, obsessed with Byzantine Sculpture in the first instance, but aren’t afraid of learning new aspects of computing without having any undergrad credit courses in it.
I’d also say that there is plenty room for people who do it the other way around – undergrads in comp sci, who then learn and get the bug for humanities research.
Terras continued that taking everything as an undergraduate would equate more to liberal arts or information science than a pure humanities degree:
As with all of these things, it depends on the make up of the individual programs. In my undergrad, I did 6 courses in my final year. If I had taken all of the ones you suggest: (historical methods, philosophy of science, linear algebra, statistics, programming, and web development) then I wouldn’t have been able to take any humanities courses! which would mean I was doing liberal arts, or information science, rather than a pure humanities degree. This will be a problem for many – just sayin’.
But yes, I think the key thing really is the *interest* and the *passion*. If your institution doesnt allow that type of courses as part of a humanities degree, you haven’t shot yourself in the foot, you just need to learn computing some other way…
Self-teaching is something that I think most people reading this blog can get behind (or commiserate with). I’m glad Terras shifted my focus away from undergraduate courses, and more on how to get a DH education.
John Walsh is known in the DH world for his work on TEI, XML, and other formal data models of humanities media. He replied:
I started undergrad as a fine arts major (graphic design) at Ohio University, before switching to English literary studies. As an art major, I was required during my freshman year to take “Comparative Arts I & II,” in which we studied mostly the formal aspects of literature, visual arts, music, and architecture. Each of the two classes occupied a ten-week “quarter” (fall winter spring summer), rather than a semester. At the time OU had a department of comparative arts, which has since become the School of Interdisciplinary Arts.
In any case, they were fascinating classes, and until you asked the question, I hadn’t really considered those courses in the context of DH, but they were definitely relevant and influential to my own work. I took these courses in the 80s, but I imagine an updated version that took into account digital media and digital representations of non-digital media would be especially useful. The study of the formal aspects of these different art forms and media and shared issues of composition and construction gave me a solid foundation for my own work constructing things to model and represent these formal characteristics and relationships.
Walsh was the first one to single out a specific humanities course as particularly beneficial to the DH agenda. It makes sense: the course appears to have crossed many boundaries, focusing particularly on formal similarities. I’d hazard that this approach is at the heart of many of the more computational and formal areas of digital humanities (but perhaps less so for those areas more aligned with new media or critical theory).
I agree web development should be in the mix somewhere, along with something like Ryan Cordell’s “Text Technologies” that would cover various representations of text/documents and a look at their production, digital and otherwise, as well as tools (text analysis, topic modeling, visualization) for doing interesting things with those texts/documents.
Otherwise, Walsh’s courses aligned with those of Underwood and Drucker.
Matt Jockers‘ expertise, like Underwoods, tends toward computational literary history and criticism. His reply was short and to the point:
The thing I see missing here are courses Linguistics and Machine Learning. Specifically courses in computational linguistics, corpus linguistics, and NLP. The later are sometimes found in the CS depts. and sometimes in linguistics, it depends. Likewise, courses in Machine Learning are sometimes found in Statistics (as at Stanford) and sometimes in CS (as at UNL).
Jockers, like Underwood, mentioned that I was missing linguistics. On the twitter conversation, Heather Froehlich pointed out the same deficiency. He and Underwood also pointed out machine learning, which are particularly useful for the sort of research they both do.
I was initially surprised by how homogeneous the answers were, given the much-touted diversity of the digital humanities. I had asked a few others to get back to me, who for various reasons couldn’t get back to me at the time, situated more closely in the new media, alt-ac, and library camps, but even the similarity among those I asked was a bit surprising. Is it that DH is slowly canonizing around particular axes and methods, or is my selection criteria just woefully biased? I wouldn’t be too surprised if it were the latter.
In the end, it seems (at least according to life-paths of these particular digital humanists), the modern digital humanist should be a passionate generalist, well-versed in their particular field of humanistic inquiry, and decently-versed in a dizzying array of subjects and methods that are tied to computers in some way or another. The path is not necessarily one an undergraduate curriculum is well-suited for, but the self-motivated have many potential sources for education.
I was initially hoping to turn this short survey into a list of potential undergraduate curricula for different DH paths (much like my list of DH syllabi), but it seems we’re either not yet at that stage, or DH is particularly ill-suited for the undergraduate-style curricula. I’m hoping some of you will leave comments on the areas of DH I’ve clearly missed, but from the view thus-far, there seems to be more similarities than differences.
5 11 2013
A few hundred years ago, I promised to talk about when not to use networks, or when networks are used improperly. With The Historian’s Macroscope in the works, I’ve decided to finally start answering that question, and this Networks Demystified is my first attempt at doing so. If you’re new here, this is part of an annoyingly long series (1 network basics, 2 degree, 3 power laws, 4 co-citation analysis, 5 communities and PageRank, 6 this space left intentionally blank, 7 co-citation analysis II). I’ve issued a lot of vague words of caution without doing a great job of explaining them, so here is the first substantive part of that explanation.
Networks are great. They allow you to do things like understand the role of postal routes in the circulation of knowledge in early modern Europe, or of the spread of the black death in the middle ages, or the diminishing importance of family ties in later Chinese governments. They’re versatile, useful, and pretty easy in today’s software environment. And they’re sexy, to boot. I mean, have you seen this visualization of curved lines connecting U.S. cities? I don’t even know what it’s supposed to represent, but it sure looks pretty enough to fund!
A really pretty network visualization. [via]
So what could possibly dissuade you from using a specific network, or the concept of networks in general? A lot of things, it turns out, and even a big subset of things that belong only to historians. I won’t cover all of them here, but I will mention a few big ones.
An Issue of Memory Loss
Okay, I lied about not knowing what the above network visualization represents. It turns out it’s a network of U.S. air travel pathways; if a plane goes from one city to another, an edge connects the two cities together. Pretty straightforward. And pretty useful, too, if you want to model something like the spread of an epidemic. You can easily see how someone with the newest designer virus flying into Texas might infect half-a-dozen people at the airport, who would in turn travel to other airports, and quickly infect most parts of the country with major airports. Transportation networks like this are often used by the CDC for just such a purpose, to determine what areas might need assistance/quarantine/etc.
The problem is that, although such a network might be useful for epidemiology, it’s not terribly useful for other seemingly intuitive questions. Take migration patterns: you want to know how people travel. I’ll give you another flight map that’s a bit easier to read.
The first thing people tend to do when getting their hands on a new juicy network dataset is to throw it into their favorite software suite (say, Gephi) and run a bunch of analyses on it. Of those, people really like things like PageRank or Betweenness Centrality, which can give the researcher a sense of important nodes in the network based on how central they are; in this case, how many flights have to go through a particular city in order to get where they eventually intend to go.
Flight patterns over U.S. [via]
Let’s look at Las Vegas. By anyone’s estimation it’s pretty important; well-connected to cities both near and far, and pretty central in the southwest. If I want to go from Denver to Los Angeles and a direct flight isn’t possible, Las Vegas seems to be the way to go. If we also had road networks, train networks, cell-phone networks, email networks, and so forth all overlaid on top of this one, looking at how cities interact with each other, we might be able to begin to extrapolate other information like how rumors spread, or where important trade hubs are.
Here’s the problem: network structures are deceitful. They come with a few basic assumptions that are very helpful in certain areas, but extremely dangerous in others, and they are the reason why you shouldn’t analyze a network without thinking through what you’re implying by fitting your data to the standard network model. In this case, the assumption to watch out for is what’s known as a lack of memory.
The basic networks you learn about, with nodes and edges and maybe some attributes, embed no information on how those networks are generally traversed. They have no memories. For the purposes of disease tracking, this is just fine: all epidemiologists generally need to know is whether two people might accidentally happen to find themselves in the same place at the same time, and where they individually go from there. The structure of the network is enough to track the spread of a disease.
For tracking how people move, or how information spreads, or where goods travel, structure alone is rarely enough. It turns out that Las Vegas is basically a sink, not a hub, in the world of airline travel. People who travel there tend to stay for a few days before traveling back home. The fact that it happens to sit between Colorado and California is meaningless, because people tend not to go through Vegas to get from one to another, even though individually, people from both states travel there with some frequency.
If the network had a memory to it, if it somehow knew not just that a lot of flights tended to go between Colorado and Vegas and between LA and Vegas, but also that the people who went to Vegas returned to where they came from, then you’d be able to see that Vegas isn’t the same sort of hub that, say, Atlanta is. Travel involving Vegas tends to be to or from, rather than through. In truth, all cities have their own unique profiles, and some may be extremely central to the network without necessarily being centrally important in questions about that network (like human travel patterns).
The same might be true of letter-writing networks in early modern Europe, my research of choice. We often find people cropping up as extremely central, connecting very important figures whom we did not previously realize were connected, only to find out that later that, well, it’s not exactly what we thought. This new central figure, we’ll call him John Smith, happened to be the cousin of an important statesman, the neighbor of a famous philosopher, and the once-business-partner of some lawyer. None of the three ever communicated with John about any of the others, and though he was structurally central on the network, he was no-one of any historical note. A lack of memory in the network that information didn’t flow through John, only to or from him, means my centrality measurements can often be far from the mark.
It turns out that in letter-writing networks, people have separate spheres: they tend to write about family with family members, their governmental posts with other officials, and their philosophies with other philosophers. The overarching structure we see obscures partitions between communities that seem otherwise closely-knit. When researching with networks, especially going from the visualization to the analysis phase, it’s important to keep in mind what the algorithms you use do, and what assumptions they and your network structure embed in the evidence they provide.
Sometimes, the only network you have might be the wrong network for the job. I have a lot of peers (me included) who try to understand the intellectual landscape of early modern Europe using correspondence networks, but this is a poor proxy indeed for what we are trying to understand. Because of the spurious structural connections, like that of our illustrious John Smith, early modern networks give us a sense of unity that might not have been present at the time.
And because we’re only looking on one axis (letters), we get an inflated sense of the importance of spatial distance in early modern intellectual networks. Best friends never wrote to each other; they lived in the same city and drank in the same pubs; they could just meet on a sunny afternoon if they had anything important to say. Distant letters were important, but our networks obscure the equally important local scholarly communities.
If there’s a moral to the story, it’s that there are many networks that can connect the same group of nodes, and many questions that can be asked of any given network, but before trying to use networks to study history, you should be careful to make sure the questions match the network.
As humanists asking humanistic questions, our networks tend to be more complex than the sort originally explored in network science. We don’t just have people connected to people or websites to websites, we’ve got people connected to institutions to authored works to ideas to whatever else, and we want to know how they all fit together. Cue the multimodal network, or a network that includes several types of nodes (people, books, places, etc.).
I’m going to pick on Elijah Meeks’ map of of the DH2011 conference, because I know he didn’t actually use it to commit the sins I’m going to discuss. His network connected participants in the conference with their institutional affiliations and the submissions they worked on together.
From a humanistic perspective, and especially from a Latourian one, these multimodal networks make a lot of sense. There are obviously complex relationships between many varieties of entities, and the promise of networks is to help us understand these relationships. The issue here, however, is that many of the most common metrics you’ll find in tools like Gephi were not created for multimodal networks, and many of the basic assumptions of network research need to be re-aligned in light of this type of use.
Part of Elijah Meeks’ map of DH2011. [via]
Let’s take the local clustering coefficient as an example. It’s a measurement often used to see if a particular node spans several communities, and it’s calculated by seeing how many of a node’s connections are connected to each other. More concretely, if all of my friends were friends with one another, I would have a high local clustering coefficient; if, however, my friends tended not to be friends with one another, and I was the only person in common between them, my local clustering coefficient would be quite low. I’d be the bridge holding the disparate communities together.
If you study the DH2011 network, the problem should become clear: local clustering coefficient is meaningless in multimodal networks. If people are connected to institutions and conference submissions, but not to one another, then everyone must have the same local clustering coefficient: zero. Nobody’s immediate connections are connected to each other, by definition in this type of network.
Local clustering coefficient is an extreme example, but many of the common metrics break down or mean something different when multiple node-types are introduced to the network. People are coming up with ways to handle these networks, but the methods haven’t yet made their way into popular software. Yet another reason that a researcher should have a sense of how the algorithms work and how they might interact with their own data.
No Network Zone
The previous examples pointed out when networks might be used inappropriately, but there are also times when there is no appropriate use for a network. This isn’t so much based on data (most data can become a network if you torture them enough), but on research questions. Networks seem to occupy a similar place in the humanities as power laws do in computational social sciences: they tend to crop up everywhere regardless of whether they actually add anything informative. I’m not in the business of calling out poor uses of networks, but a good rule of thumb on whether you should include a network in your poster or paper is to ask yourself whether its inclusion adds anything that your narrative doesn’t.
Alternatively, it’s also not uncommon to see over-explanations of networks, especially network visualizations. A narrative description isn’t always the best tool for conveying information to an audience; just as you wouldn’t want to see a table of temperatures over time when a simple line chart would do, you don’t want a two-page description of communities in a network when a simple visualization would do.
This post is a bit less concise and purposeful than the others in this series, but stay-tuned for a revamped (and hopefully better) version to show up in The Historian’s Macroscope. In the meantime, as always, comments are welcome and loved and will confer good luck on all those who write them.
5 11 2013
Submissions for the 2014 Digital Humanities conference just closed. It’ll be in Switzerland this time around, which unfortunately means I won’t be able make it, but I’ll be eagerly following along from afar. Like last year, reviewers are allowed to preview the submitted abstracts. Also like last year, I’m going to be a reviewer, which means I’ll have the opportunity to revisit the submissions to DH2013 to see how the submissions differed this time around. No doubt when the reviews are in and the accepted articles are revealed, I’ll also revisit my analysis of DH conference acceptances.
To start with, the conference organizers received a record number of submissions this year: 589. Last year’s Nebraska conference only received 348 submissions. The general scope of the submissions haven’t changed much; authors were still supposed to tag their submissions using a controlled vocabulary of 95 topics, and were also allowed to submit keywords of their own making. Like last year, authors could submit long papers, short papers, panels, or posters, but unlike last year, multilingual submissions were encouraged (English, French, German, Italian, or Spanish). [edit: Bethany Nowviskie, patient awesome person that she is, has noticed yet another mistake I've made in this series of posts. Apparently last year they also welcomed multilingual submissions, and it is standard practice.]
Digital Humanities is known for its collaborative nature, and not much has changed in that respect between 2013 and 2014 (Figure 1). Submissions had, on average, between two and three authors, with 60% of submissions in both years having at least two authors. This year, a few fewer papers have single authors, and a few more have two authors, but the difference is too small to be attributable to anything but noise.
Figure 1. Number of authors per paper.
The distribution of topics being written about has changed mildly, though rarely in extreme ways. Any changes visible should also be taken with a grain of salt, because a trend over a single year is hardly statistically robust to small changes, say, in the location of the event.
The grey bars in Figure 2 show what percentage of DH2014 submissions are tagged with a certain topic, and the red dotted outlines show what the percentages were in 2013. The upward trends to note this year are text analysis, historical studies, cultural studies, semantic analysis, and corpora and corpus activities. Text analysis was tagged to 15% of submissions in 2013 and is now tagged to 20% of submissions, or one out of every five. Corpus analysis similarly bumped from 9% to 13%. Clearly this is an important pillar of modern DH.
Figure 2. Topics from DH2014 ordered by the percent of submissions which fall in that category. The red dotted outlines represent the percentage from DH2013.
I’ve pointed out before that History is secondary compared to Literary Studies in DH (although Ted Underwood has convincingly argued, using Ben Schmidt’s data, that the numbers may merely be due to fewer people studying history). This year, however, historical studies nearly doubled in presence, from 10% to 17%. I haven’t yet collected enough years of DH conference data to see if this is a trend in the discipline at large, or more of a difference between European and North American DH. Semantic analysis jumped from 1% to 7% of the submissions, cultural studies went from 10% to 14%, and literary studies stayed roughly equivalent. Visualization, one of the hottest topics of DH2013, has become even hotter in 2014 (14% to 16%).
The most visible drops in coverage came in pedagogy, scholarly editions, user interfaces, and research involving social media and the web. At DH2013, submissions on pedagogy had a surprisingly low acceptance rate, which combined the drop in pedagogy submissions this year (11% to 8% in “Digital Humanities – Pedagogy and Curriculum” and 7% to 4% in “Teaching and Pedagogy”) might suggest a general decline in interest in the DH world in pedagogy. “Scholarly Editing” went from 11% to 7% of the submissions, and “Interface and User Experience Design” from 13% to 8%, which is yet more evidence for the lack of research going into the creation of scholarly editions compared to several years ago. The most surprising drops for me were those in “Internet / World Wide Web” (12% to 8%) and “Social Media” (8.5% to 5%), which I would have guessed would be growing rather than shrinking.
The last thing I’ll cover in this post is the author-chosen keywords. While authors needed to tag their submissions from a list of 95 controlled vocabulary words, they were also encouraged to tag their entries with keywords they could choose themselves. In all they chose nearly 1,700 keywords to describe their 589 submissions. In last year’s analysis of these keywords, I showed that visualization seemed to be the glue that held the DH world together; whether discussing TEI, history, network analysis, or archiving, all the disparate communities seemed to share visualization as a primary method. The 2014 keyword map (Figure 3) reveals the same trend: visualization is squarely in the middle. In this graph, two keywords are linked if they appear together on the same submission, thus creating a network of keywords as they co-occur with one another. Words appear bigger when they span communities.
Figure 3. Co-occurrence of DH2014 author-submitted keywords.
Despite the multilingual conference, the large component of the graph is still English. We can see some fairly predictable patterns: TEI is coupled quite closely with XML; collaboration is another keyword that binds the community together, as is (obviously) “Digital Humanities.” Linguistic and literature are tightly coupled, much moreso than, say, linguistic and history. It appears the distant reading of poetry is becoming popular, which I’d guess is a relatively new phenomena, although I haven’t gone back and checked.
This work has been supported by an ACH microgrant to analyze DH conferences and the trends of DH through them, so keep an eye out for more of these posts forthcoming that look through the last 15 years. Though I usually share all my data, I’ll be keeping these to myself, as the submitters to the conference did so under an expectation of privacy if their proposals were not accepted.
[edit: there was some interest on twitter last night for a raw frequency of keywords. Because keywords are author-chosen and I'm trying to maintain some privacy on the data, I'm only going to list those keywords used at least twice. Here you go (Figure 4)!]
Figure 4. Keywords used in DH2014 submissions ordered by frequency.
31 10 2013
[edit: I've been told the word I'm looking for is actually preservation, not sustainability. Whoops.]
Sustainability’s a tricky word. I don’t mean whether the scottbot irregular is carbon neutral, or whether it’ll make me enough money to see me through retirement. This post is about whether scholarly blog posts will last beyond their author’s ability or willingness to sustain them technically and financially.
A colleague approached me at a conference last week, telling me she loved one of my blog posts, had assigned it to her students, and then had freaked out when my blog went down and she didn’t have a backup of the post. She framed it as being her fault, for not thinking to back up the material.
Of course, it wasn’t her fault that my site was down. As a grad student trying to save some money, I use the dirt-cheap bluehost for hosting my site. It goes down a lot. At this point, now that I’m blogging more seriously, I know I should probably migrate to a more serious hosting solution, but I just haven’t found the time, money, or inclination to do so.
This is not a new issue by any means, but my colleague’s comment brought it home to me for the first time. A lot has already been written on this subject by archivists, I know, but I’m not directly familiar with any of the literature. As someone who’s attempting to seriously engage with the scholarly community via my blog (excepting the occasional Yoda picture), I’m only now realizing how much of the responsibility of sustainability in these situations lies with the content creator, rather than with an institution or library or publishing house. If I finally decide to drop everything and run away with the circus (it sometimes seems like the more financially prudent option in this academic job market), *poof* the bulk of my public academic writings go the way of Keyser Söze.
So now I’m going to you for advice. If we’re aiming to make blogs good enough to cite, to make them countable units in the scholarly economy that can be traded in for things like hiring and tenure, to make them lasting contributions to the development of knowledge, what are the best practices for ensuring their sustainability? I feel like I haven’t been treating this bluehost-hosted blog with the proper respect it needs, if the goal of academic respectability is to be achieved. Do I self-archive every blogpost in my institution’s dspace? Does the academic community need to have a closer partnership with something like archive.org to ensure content persistence?