Tag Archives: Digital Humanities

Ada Lovelace Day 2013

October 15th marks Ada Lovelace Day, an annual celebration of the achievements of women in science, technology, engineering and maths. As I read through posts commemorating the day, it got me reflecting on my own experience. It’s not just that I admire Ada Lovelace and the women that followed after her. It’s that I quite literally wouldn’t be here without them.

My mom, Bridget Baird, went to an all-women’s college in the late 1960s where she considered majoring in philosophy before switching to mathematics. After getting her PhD, she took a job in the early 1980s at Connecticut College in the math department. She got interested in computer programming, and eventually moved into a joint appointment in the computer science department. Over a three-decade career, her curiosity led her (and her thousands of students along with her) to the intersection of computer science with disciplines as far afield as archaeology, music, dance, and art. Along the way she faced the kinds of systemic discrimination that plagued the entire cohort of women entering male-dominated fields in the 1970s and 1980s. In other ways, she was lucky to have grown up during a time of transition when women began carving out new possibilities to enter those fields. She has spent her entire career mentoring female students and colleagues while vocally pushing her institution and discipline to take a more active role in tackling gender equity.

Although I missed the boat entirely on my mom’s math gene, she did manage to impress on me her fascination with applying computers to solve problems. Five years ago I wrote personal statements for history graduate programs structured around my interest in using technology to study the past. My mom since helped me learn how to program and we eventually ended up collaborating on a couple of projects. I’m one of the few graduate students I know who can call their mother to ask her about Thanksgiving plans and Python modules. I am, in ways I can’t even begin to articulate, a direct beneficiary of the legacy left by women like Ada Lovelace.

Which is why I oscillate between hope and discouragement when I look at around my own disciplinary homes of history and the digital humanities. On the one hand, women have made significant inroads in both fields. There are roughly equal numbers of male and female graduate students in my department. Many of the thought leaders and rising stars of the digital humanities are women, with opportunities and support growing all the time. The kinds of daily overt sexism faced by my mom and other women in her generation have, for the most part, gone the way of transistor radios. But that’s the problem: what remains is an insidious, covert sexism that is much, much harder to uproot.

And it’s everywhere. The proportion of female faculty in history departments is far lower than other fields, with the proportion of new female PhDs hovering stubbornly around 40%. Male historians continue to enjoy more time to spend on the kind of research that will get them tenure (as opposed to female historians spending more time on teaching and instruction), while men and women express completely different perceptions of gender equity at their institutions. The digital humanities have unfortunately inherited many of the gender problems endemic to computer science. These problems rear their ugly head everywhere, from the assumptions of a privileged male coding culture to the language of “hard” STEM fields vs. “soft” humanities. When I look around the room at digital humanities meetings and conferences I see the faces of a whole lot of people who look a whole lot like me. At a digital humanities conference on women’s history, though, I found that those same faces all but disappeared. I think about my mom every time I watch a female student grow increasingly silent during a discussion section or read the names of this year’s Nobel Prize winners. We can do better.

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Learning by Doing: Labs and Pedagogy in the Digital Humanities

The digital humanities adore labs. Labs both symbolize and enable many of the field’s overarching themes: interdisciplinary teamwork, making/building, and the computing process itself. Labs give digital humanists a science-y legitimation that, whether we admit it or not, we find appealing. Labs aren’t necessary for doing digital humanities research, but in terms of infrastructure, collaboration, and institutional backing they certainly help. Along with “collaboration” and “open” (and possibly “nice“), “lab” is one of the field’s power words. With a period of accelerated growth over the past five years, world-wide digital humanities labs and centers now run into the hundreds. We overwhelmingly focus on labs in this kind of context: labs as physical research spaces. I’d like to move away from this familiar ground to discuss the role of lab assignments within a digital humanities curriculum. While reflecting on my own recent experience of designing and using labs in the classroom, I realized it spoke to many of the current issues facing the digital humanities.

Let me start with some background. This past autumn I taught my first college course, “The Digital Historian’s Toolkit: Studying the West in an Age of Big Data.” It was one of Stanford History Department’s Sources & Methods seminars, which are classes aimed at history majors to get them working intensively with primary sources. When I was designing my course a year ago, I decided to blend a digital humanities curriculum with more traditional historical pedagogy. Under the broad umbrella of the nineteenth-century American West, I used a specific historical theme each week (mining, communications, tourism, etc.) to tie together both traditional analysis and digital methodology. As part of this, over five different class periods students met in the Center for Spatial and Textual Analysis to complete a weekly lab assignment.

In designing the course, I wrestled with a problem that faces every digital humanist: the balancing of “traditional” (for lack of a better term) and “digital.” How much of my curriculum should follow a seminar model based on reading and discussion? How much should it follow a lab model based on technical tools and techniques? As is often the case, pragmatism partially informed my decision. Because my class was part of a required series of courses offered by the department, I couldn’t simply design a full-blown digital humanities methods course. It had to have a strong historical component in order to get approved. This juggling act is not uncommon for digital humanists. But more philosophically, I believed that digital tools were best learned in the context of historical inquiry. An overarching theme (in my case, the late nineteenth-century West) helped answer the question of why a student was learning a particular piece of software. Without it, digital pedagogy can stray into the bugaboo waved about by skeptics: teaching technology for technology’s sake.

I designed my labs with three goals in mind. First, I wanted my students to come away with at least an introduction to technical skills they wouldn’t otherwise get in a typical history course. Given my background, I focused largely on GIS, textual analysis, and visual design. I didn’t expect my students to become geospatial technicians in ten weeks, but I did want them to try out these kinds of methods and understand how they could be applied to historical problems. This first goal speaks to the alarmist rhetoric of a “crisis in the humanities,” of falling enrollments and shrinking budgets and growing irrelevance. In this caricature, the digital humanities often get remade as a life-boat for a sinking ship. This view is obviously overblown. But it is important to remember that the vast majority of our students are not going to end up as professors of history, literature, or philosophy. While there is a strong case to be made for the value of the humanities, I also think we need to do a better job of grafting other kinds of skills onto the field’s reading/writing/thinking foundation.

Second, I wanted students to learn technical skills as part of a larger intellectual framework. I pursued this in part by assigning specific techniques to answer larger questions. For instance, how does Mark Twain’s western novel Roughing It compare to other iconic nineteenth-century works of literature? Instead of assigning thousands of pages of text, I had my students use topic modeling to compare Roughing It to other books such as Uncle Tom’s Cabin and Little Women. But labs were also an effective way to concretize some of the contemporary issues swirling around technology. In one of the labs, students applied different kinds OCR software to a sampling of pages from an Overland Trail diary they had read earlier in the week. This gave them a chance to peer behind the curtain of large-scale digitization projects. When you experience first-hand just how many words and characters the OCR process can miss, it makes you think more critically about resources like Google Books or LexisNexis. Teaching in the digital humanities should, in part, force students to think critically about the issues surrounding the tools we use: copyrightaccessmarginalization.

Finally, I wanted students to learn by doing. There’s a certain passive mode of learning endemic to so many humanities courses: go to lectures, write a few papers, study for an exam, make comments in discussion. Student passivity can be inherent to both the pedagogical form itself and how it’s practiced, as anyone who has sat in a lecture hall or watched a student coast through discussion can tell you. Don’t get me wrong: bad labs can be just as passive as lectures. But done right, they emphasize active learning based on immediate feedback. As much as I’ve soured on the term “hacking” and all the privileged baggage it can carry, it is a useful term to describe the type of learning I want my students to engage in. Try something out. If it doesn’t work, try something else. Under this rubric, mistakes are a necessary part of the process. Feedback is more immediate in a way that enables exploration, tinkering, tangents, and restarts. It’s a lot harder to do this with traditional assignments; trying out something new in a paper is riskier than trying out something new in a lab.

This last goal proved the hardest to meet and constitutes one of the major hurdles facing digital humanities pedagogy. We want to teach digital methods not for their own sake, but to fit them within a broader framework, such as how they help us understand the past. But to get to that point, students need to make a fairly substantial investment of time and energy into learning the basics of a particular tool or technique. I tried to scaffold my lab assignments so that they became less and less prescriptive and more and more open-ended with each passing week. The idea was that students needed heavy doses of step-by-step instruction when they were still unfamiliar with the technology. My first lab, for instance, spelled out instructions in excruciating detail. Unfortunately, this led to exactly the kind of passive learning I wanted to avoid. I liken it to the “tutorial glaze” – focusing so much on getting through individual tasks that you lose track of how they all fit together or how you would apply them beyond the dataset at hand. The ability to teach early-stage technical skills involves a litany of pedagogical challenges that humanities instructors are simply not used to tackling.

By contrast, my final lab gave students a dataset (a map of Denver and enumeration district data from the 1880 census) and asked them to formulate and then answer a historical question through GIS. By nearly any metric – enthusiasm, results, feedback – this proved to be the most effective lab. It forced students to engage in the messy process of digital history: exploring the data enough to formulate a question, returning to the data to answer that question, realizing the data can’t even begin to answer that question, formulating a different question, figuring out how to answer it, and deciding how to visualize an argument. I was even more satisfied with their reflections on the process. Some described the frustrations that came with discovering the limits or gaps in census data. Others remarked on how their own mapmaking decisions, such as changing classification breaks or using different symbology, could completely alter the presentation of their argument. It’s one thing for students to read an essay by J.B. Harley on the subjectivity of maps (which they did). It’s another for students to experience the subjective process of map-making for themselves. Learning by doing: this is what was labs are all about.

To try and help others who want to integrate labs into their curriculum, I’ve made the labs and datasets available to download on the course website. Even as I posted them, though, I was reminded of one last problem facing the digital humanities: the problem of ephemerality. I spent hours and hours designing labs that will likely be unusable in a matter of years. Some of them require expensive software licenses, others rely on tools that could fall completely out of development. That’s one of the downside of labs. Ten years from now, I’ll still be able to re-use my lesson plan for discussing Roughing It. The lab on topic-modeling Twain and other novelists? Doubtful. But ephemerality is one of the necessary costs of teaching digital humanities. Because labs, and the broader pedagogical ethos of the digital humanities they embody, are ultimately worth it.

Coding a Middle Ground: ImageGrid

Openness is the sacred cow of the digital humanities. Making data publicly available, writing open-source code, or publishing in open-access journals are not just ideals, but often the very glue that binds the field together. It’s one of the aspects of digital humanities that I find most appealing. Despite this, I have only slowly begun to put this ideal into practice. Earlier this year, for instance, I posted over one hundred book summaries I had compiled while studying for my qualifying exams. Now I’m venturing into the world of open-source by releasing a program I used in a recent research project.

The program tries to tackle one of the fundamental problem facing many digital humanists who analyze text: the gap between manual “close reading” and computational “distant reading.” In my case, I was trying to study the geography within a large corpus of nineteenth-century Texas newspapers. First I wrote Python scripts to extract place-names from the papers and calculate their frequencies. Although I had some success with this approach, I still ran into the all-too-familiar limit of historical sources: their messiness. Namely, nineteenth-century newspapers are extremely challenging to translate into machine-readable text. When performing Optical Character Recognition (OCR), the smorgasbord nature of newspapers poses real problems. Inconsistent column widths, a potpourri of advertisements, vast disparities in text size and layout, stories running from one page to another – the challenges go on and on and on. Consequently, extracting the word “Havana” from OCR’d text is not terribly difficult, but writing a program that identifies whether it occurs in a news story versus an advertisement is much harder. Given the quality of the OCR’d text in my particular corpus, deriving this kind of context proved next-to-impossible.

The messy nature of digitized sources illustrates a broader criticism I’ve heard of computational distant reading: that it is too empirical, too precise, and too neat. Messiness, after all, is the coin of the realm in the humanities – we revel in things like context, subtlety, perspective, and interpretation. Computers are good at generating numbers, but not so good at generating all that other stuff. My computer program could tell me precisely how many times “Chicago” was printed in every issue of every newspaper in my corpus. What it couldn’t tell me was the context in which it occurred. Was it more likely to appear in commercial news? Political stories? Classified ads? Although I could read a sample of newspapers and manually track these geographic patterns, even this task proved daunting: the average issue contained close to one thousand place-names and stretched more than 67,000 words (or, longer than Mrs. Dalloway, Fahrenheit 451, and All Quiet on the Western Front). I needed a middle ground. I decided to move backwards, from the machine-readable text of the papers to the images of the newspapers themselves. What if I could broadly categorize each column of text according both to its geography (local, regional, national, etc.) and its type of content (news, editorial, advertisement, etc.)? I settled on the idea of overlaying a grid onto the page image. A human reader could visually skim across the page and select cells in the grid to block off each chunk of content, whether it was a news column or a political cartoon or a classified ad. Once the grid was divided up into blocks, the reader could easily calculate the proportions of each kind of content.

My collaborator, Bridget Baird, used the open-source programming language Processing to develop a visual interface to do just that. We wrote a program called ImageGrid that overlaid a grid onto an image, with each cell in the grid containing attributes. This “middle-reading” approach allowed me a new access point into the meaning and context of the paper’s geography without laboriously reading every word of every page. A news story on the debate in Congress over the Spanish-American War could be categorized primarily as “News” and secondarily as both “National” and “International” geography. By repeating this process across a random sample of issues, I began to find spatial patterns.

Grid with primary categories as colors and secondary categories as letters

For instance, I discovered that a Texas paper from the 1840s dedicated proportionally more of its advertising “page space” to local geography (such as city grocers, merchants, or tailors) than did a later paper from the 1890s. This confirmed what we might expect, as a growing national consumer market by the end of the century gave rise to more and more advertisements originating from outside of Texas. More surprising, however, was the pattern of international news. The earlier paper contained three times as much foreign news (relative “page space” categorized as news content and international geography) as did the later paper in the 1890s. This was entirely unexpected. The 1840s should have been a period of relative geographic parochialism compared to the ascendant imperialism of the 1890s that marked the United States’s noisy emergence as a global power. Yet the later paper dedicated proportionally less of its news to the international sphere than the earlier paper. This pattern would have been otherwise hidden if I had used either a close-reading or distant-reading approach. Instead, a blended “middle-reading” through ImageGrid brought it into view.

We realized that this “middle-reading” approach could be readily adapted not just to my project, but to other kinds of humanities research. A cultural historian studying American consumption might use the program to analyze dozens of mail-order catalogs and quickly categorize the various kinds of goods – housekeeping, farming, entertainment, etc. – marketed by companies such as Sears-Roebuck. A classicist could analyze hundreds of Roman mosaics to quantify the average percentage of each mosaic dedicated to religious or military figures and the different colors used to portray each one.

Inspired by the example set by scholars such as Bethany NowviskieJeremy Boggs, Julie Meloni, Shane Landrum, Tim Sherratt, and many, many others, we released ImageGrid as an open-source program. A more detailed description of the program is on my website, along with a web-based applet that provides an interactive introduction to the ImageGrid interface. The program itself can be downloaded either on my website or on its GitHub repository, where it can be modified, improved, and adapted to other projects.

Kobe Bryant and the Digital Humanities

What does one of the most successful and polarizing basketball players in history have to do with the digital humanities?

For those that don’t follow the NBA, Kobe Bryant is famous for a host of accomplishments: winning five championships, league MVP honors, and an Olympic gold medal, leading the league in scoring twice, winning the All-Star dunk contest, and scoring the second most points in a single game in history. He has also been accused over the years of placing personal success ahead of the team, undermining teammates and coaches, and most notoriously, of sexual assault in 2003. From a basketball standpoint, however, one of the most enduring aspects of Bryant’s career has been an overwhelming consensus of his ability as a “clutch” player. There exists a widespread perception that no other basketball player on earth is better at the end of close games. Both NBA players and general managers have repeatedly and overwhelmingly voted Bryant as the player they would want taking a shot with the game on the line. Bryant’s name and legacy have become entwined with the word “clutch.”

Unfortunately, this is a flawed narrative. Henry Abbott recently wrote a blistering (and persuasive) analysis of Bryant’s abilities as a “clutch” player. Abbott concludes that, by nearly every statistical measure he examined, Bryant is not the best in the world at scoring points at the end of close games. Depending on the metric, Bryant is somewhere between decent and very good, but nowhere close to the best. Perhaps most damningly, the effectiveness of his team’s offense (the best in the league during Bryant’s tenure) plummets at the end of games.

So the question remains: what does Kobe Bryant have to do with the digital humanities?

The fault line in the basketball world over Kobe Bryant’s “clutchness” largely falls between those that evaluate Bryant’s ability by what they see and those that evaluate his ability by what they measure. For someone watching Bryant, no other player has as many breathtaking, memorable game-winning shots and no other player looks as graceful and impressive while doing it. I draw a parallel between this qualitative analysis with more traditional humanistic research: we read our sources and look for meaningful or interesting patterns that jump out at us. On the other side of the basketball fault-line stands a young but growing movement that advocates for more rigorous statistical analysis of basketball, in the same vein as the sabermetric “Moneyball” movement in baseball. For these stat-heads, the seductive aesthetic appeal of Bryant’s game-winning shots hides the less glamorous reality: that Bryant misses those game-winning shot attempts at an extremely high rate. And this is the side of the debate that I would compare to the digital humanities.

The analogy isn’t perfect. Much of the work being done in the digital humanities field is not, in fact, quantitative (and making the comparison brings to mind the less-successful turn towards quantitative history in the 1960s and 1970s). But the analogy does have  some useful parallels. Like the stats movement in the basketball world, digital humanities has a lengthy history but has only recently begun to gain traction across the wider academy. Like the stats movement in the basketball world, digital humanities is occasionally seen as threatening or, at the very least, promising too much. Like the stats movement in the basketball world, there are those in digital humanities that revel in revisionism and using new techniques to challenge conventional narratives. And like the stats movement in the basketball world, there are divisions within the digital humanities over method, approach, and emphasis.

One of the most important parallels to be drawn is how the digital humanities are increasingly being used to strengthen (rather than replace) traditional humanistic study, just as advanced statistics are being used in the NBA to strengthen analysis. In the past, a basketball player would be evaluated by a handful of traditional statistics, perhaps most importantly: how many points do they score? Today, teams and scouts are looking at more advanced metrics: for instance, how efficiently do they score those points? In the same vein, traditional literary history might look at a handful of canonical works in order to draw broad conclusions about, say, early-19th century British fiction. Today, advocates of distant reading are measuring trends across hundreds or thousands of early-19th century British novels beyond the canonical authors. Most of these digital researchers would continue to acknowledge the literary importance of Charles Dickens over a barely-published contemporary novelist, just as most stat-heads would acknowledge the importance of a player that scores a moderately-efficient 30 points per game over a player that scores a hyper-efficient 5 points per game.

Comparing the two also highlights their limitations. Some aspects of basketball can’t be measured, such as whether or not a player is a good teammate or how likely they are to stay motivated after receiving a contract or whether they’re likely to end up injured. Similarly, human experience can be an elusive target to study with technology. Charting the prevalence of certain phrases across time using Google NGrams offers, at best, a largely superficial indicator that requires careful and more extensive investigation, while cataloging every slave ship voyage might serve to mute and depersonalize the particularities of individual slaves.

In both the statistical movement in basketball and the digital turn in the humanities, new approaches allow for new questions. Henry Abbott and others have not “proven” that Kobe Bryant shouldn’t take the last shot of a game, but they have raised important questions: would Bryant’s team be better served by using him as a decoy? More broadly, is the long-standing convention of putting the ball into the hands of your best player in an isolation situation at the end of the game even a good idea? Using digital methodologies in the humanities can also serve to pose new kinds of questions, but I think the field should model itself more explicitly after the statistical basketball community in having specific questions drive those methodologies. There is a tendency to build tools and ask research questions later. This is useful, but I’d also like to see more focused questions along the lines of “Is Kobe Bryant a clutch player?” Those of us who advocate for the use of digital tools and techniques in the humanities could benefit from taking a break from the library and turning towards the basketball court.

The Launch of Tooling Up

Today marks the public launch of a project called Humanities 3.0: Tooling Up for Digital Humanities. Over the past several months I’ve been working on Tooling Up at the Bill Lane Center for the American West. The project was originally conceived in conversation with Jon Christensen, director of the center, as an outreach initiative that would offer an accessible introduction to the realm of digital humanities. With generous funding from the University’s Presidential Fund for Innovation in the Humanities, Andrew Robichaud, Rio Akasaka, Jon, and myself began work last summer on a two-track project.

The first track is a series of online essays that explore different themes and issues within digital humanities, written in a journalistic style and aimed at a graduate student or faculty member with little to no exposure to digital scholarship or research. Each essay (there will eventually be a total of seven) deals with a particular topic within digital humanities – file and data management, digital archives, text analysis, etc. The essays are written primarily by Andy, a fellow history graduate student and DH-newcomer who did a phenomenal job of tackling topics that were outside of his comfort zone. Andy’s presence brought the added benefit of helping us all to better tailor the essays towards their intended audience: the humanities scholar who, for instance, doesn’t know what XML stands for, has only vaguely heard of Zotero, and is puzzled as to how Twitter would ever be useful for an historian. The second track of Tooling Up will take place in the spring quarter through a seminar/workshop series specifically for Stanford students and faculty. The workshops will mirror the essays by providing an in-person introduction to some of “the basics” of digital humanities.

Conceptualizing and then implementing Tooling Up forced us to grapple with a lot of issues. First, what was the project’s audience? We settled on not trying to be all things to all people. The content of Tooling Up is going to be painfully basic for the majority of people that identify themselves as digital humanists. Meanwhile, those in the #alt-ac world might be disappointed in its audience tilt towards traditional academics. And, of course, there are an inordinate number of references to Stanford examples and projects. But in the end we felt that focusing on the crowd that we knew best would allow us to deliver the most effective and coherent content.

The second issue that emerged was one of ephemerality. In a way that is markedly different from other fields, digital humanities are most commonly linked to tools, whether building them or using them, and this is reflected in the very name of our project. It is difficult to avoid ArcGIS when talking about spatial analysis or Zotero when talking about file management. But in the digital age, tools rapidly become obsolete. When Andy and I were discussing what to include in an essay section on building an online community, Delicious came to mind as an example of social bookmarking. As of the end of 2010, however, the site’s entire existence is up in the air. Ephemerality. Instead of emphasizing specific tools, therefore, we decided to use broader strokes: the basic concepts, themes, or issues surrounding different topics that will (hopefully) prove more enduring.

Finally, the issue of authority. None of us working on the project would consider ourselves experts in any one of the topics discussed in Tooling Up, much less all of them. We did our best to consult other people at Stanford who we did consider experts in those areas, but the nature of this kind of project is that it is going to always feel somewhat incomplete. In that vein, we have tried to make the project fluid and ongoing. Essays will be posted as they are finished and we encourage any and all readers to leave feedback on the site’s pages – commentary that we hope will become crucial components of the essays themselves.

Playing Well With Others

One of the sharper distinctions between the digital humanities and traditional scholars is an acceptance and emphasis on collaboration. Lisa Spiro has written several convincing posts that detail how scholars in the digital humanities are far more likely to work together and co-author essays, along with some examples of collaborative projects. At the NEH’s Office for Digital Humanities, the first requirement for applying to a grant for a fellowship at a Digital Humanities Center is to: “support innovative collaboration on outstanding digital research projects.” Meanwhile, many disciplines within the humanities cling to the notion of the individual scholar. Cathy Davidson of HASTAC tells the story of job-seeking and being told that collaborative work didn’t “count” as legitimate scholarship: “I felt like Hester Prynne wearing her Scarlet A . . . for Adulterous Authorship.” The academy remains enamored with putting a single face and a single name to research; the vast majority of the annual prizes given by the AHA are presented to individual historians for individual work.

The reasons for this distinction are easy to understand. Most digital humanities initiatives are inherently multidisciplinary. There are those among us lucky or hard-working enough to possess both “soft” humanistic talent and “hard” technical skills, but for the majority of us it is much more efficient and effective to split the workload of multiple, and often very different, approaches between more than one person. Why spend six months trying to master the intricacies of MySQL when you can team up with a colleague who already knows how to implement it? Teaming up with other people across disciplines is a form of self-preservation that saves everyone time and energy.

Another reason for the distinction often stems from the basic nature of the projects – many digital humanists have focused on building tools, online collections, and interactive media. Whereas as most academic monographs are aimed at an audience of fellow academics, these projects are inherently designed with a broader public in mind. With that overarching goal, collaboration during the production phase becomes an almost instinctive (and necessary) pursuit. Similarly, scholarly specialization leads to (often) intense intellectual turf wars. If you are struggling to make your academic mark on a very specific focus within a very specific sub-field, other people working on that same field can often seem more like a threat than a resource. These jealously guarded barriers are less prevalent within the digital humanities community, given its emphasis on greater transparency and a broader scope of study.

This is not to say that traditional humanists are allergic to collaboration. Established (read: tenured) professors are often much more willing to edit volumes, co-author essays, and work together on research projects. When you are a successful author and Harvard historian like Jill Lepore, you can afford to take a chance and co-write a work of historical fiction. An associate professor at a small state school struggling to get tenure? Not so much. Younger scholars are still plagued by the never-ending issue of digital scholarship not “counting” as a valid accomplishment.

Most graduate (particularly Ph.D) programs in the humanities simply do not train their students to play well (or at all) with others. Writing a dissertation is still viewed as an infamously lonesome pursuit. Doing so establishes your credentials as an individual scholar capable of producing original work. Unfortunately, this not only reinforces the conception that anything other than individual research is somehow less valued, but it also does a terrible job of preparing students to do any kind of future collaborative work. Learning how to take notes in an archive or write manuscript chapters are critical skills, but so is learning how to delegate tasks to research partners or co-author a grant proposal.

There is no reason why the traditional humanities cannot begin to embrace scholarly collaboration. Even for those with no interest in digital initiatives, increased collaboration creates a ripple effect. There are the obvious benefits: different perspectives add richness and depth to studies, a division of labor and specialization can lead to greater efficiency, and more collaborators often facilitates future connections across otherwise-insular academic networks. Almost every scholar has the story of a single conversation, comment, or idea from a colleague, friend, or family member sparking a revelation or major advancement in their work. Official collaboration only magnifies this effect, and the academy as a whole would benefit.

Collaboration is not a cure-all, and it presents its own set of quite-formidable challenges. As every high-schooler working on a group project or cubicle-dweller sitting in a meeting can tell you, working with other people can often be a frustrating experience. How do you divide up responsibilities, reconcile different opinions, share both criticism and credit? A professor of literature sitting across the table from a computer scientist will probably have a lot of trouble communicating effectively with each other. All of these issues have the potential to be even sharper inside the humanities, where most scholars have been given little to no official instruction or practical experience in how to work together. Nevertheless, the potential for concerted collaboration to spur on academic discovery within the humanities is simply too high to ignore.

Scattered Links – 3/16/2009

I’ve been closely following the history blogging roundtable examining Judith Bennett’s History Matters: Patriarchy and the Challenge of Feminism. Notorious Ph.D., Girl Scholar kicked things off with Should politics be historical? Should history be political? Then Historiann kept the ball rolling with Who indeed is afraid of the distant past (and who says it’s distant, anyway)? A call to arms. This week Claire Potter at Tenured Radical posted part three, Teach This Book!, with part four appearing soon at Blogenspiel. I’ve found the series instructive, given my embarrassing lack of knowledge of historiography in general, and feminist (not to mention medieval feminist) historiography in particular. A lively comment-debate about generational issues followed Notorious Ph.D.’s posting, which Historiann expounded upon in part two (and included an interesting suggestion of social history’s potential for comparative women’s studies). Tenured Radical delves into why feminist historians might gravitate towards more recent history, while championing queer history as a partial solution to some issues that Bennett raises. The history/academia blogosphere could benefit from more roundtables such as these.

Deviant Art supplies an amusing cartographic comic on the progression of World War II. My favorite part? “We talked about this before, mon ami.”

Lisa Spiro at Digital Scholarship in the Humanities gives a great two-part wrap-up of Digital Humanities developments in 2008. Part One sounds a triumphant note, including “Emergence of Digital Humanities” and “Community and collaboration,” while Part Two is more sobering, discussing continued resistance to open access and other new scholarly models, along with the erroneous and Grinch-like litigation by EndNote against Zotero.

Scientists compiled a clickstream map of “scientific activity” (along with other disciplines) that creates a visualization of how users moved from one academic journal to another. The visualization shows how different disciplines tend to cluster around one another, and I was impressed at the degree of interaction in the humanities and social sciences (although I would have loved to see more fluidity between humanities and more “hard” disciplines).

It reminded me of Sterling Fluharty’s insightful take on using quantitative methods to rank history journals based on citations, which the clickstream map avoided due to inconsistent nature of citations across disciplines.

Finally, the Economist’s Technology Quarterly profiles Brewster Kahle in “The Internet’s Librarian” and his quest to build “Alexandria 2.0,” a free digital archive of human knowledge.