A combination of human error (my own), accruing chaos, the complexities of WordPress, and the awful-but-cheap hosting solution that is bluehost.com. As many noticed, the site’s been slowing down, interactive elements like my photo gallery stopped working, and by last week, pages would go dark for hours at a time. By this week, bluehost no longer allowed me ftp or cpanel access. So yesterday I took my business to ReclaimHosting.com, the hands-down best (and friendliest) hosting service for personal and small-scale academic websites.
I still haven’t figured out what finally did it in, but with so many moving parts, it seemed better to start fresh than repair the beast. I’m currently working on a jekyll static website; this new wordpress blog you’re reading now is an interim solution. However, I couldn’t just cut my losses and start over, since I’ve put a lot of my soul into the 100+ blog posts & pages I’ve written here since 2009.
More importantly, my site has been cited in dozens of articles, and appears on the syllabus of hundreds of courses, DH and otherwise. If I delete the content, I’m destroying part of the scholarly record, and potentially ruining the morning of professors who assign my blog posts as reading, only to find out at the last minute that it no longer exists.
Here lies the problem. Because I no longer had back-end access to my website, I could not download my content through the usual channels. Because of the peculiarities of my various WordPress customizations, not worth detailing here, I could not use a plugin to export my site and its contents.
Since I wanted the form of my site preserved for the scholarly record, the only solution I could come up with was to crawl my entire site, externally, and download static html versions of each page on scottbot.net as it used to exist.
This is where the double-meaning of fix, described above, comes into play. I wanted the site functioning again, not broken, but I also wanted to preserve the old website as it existed at the URLs everyone has already linked to. For example, a bunch of syllab(uses|i) link to http://www.scottbot.net/HIAL/?tag=networks-demystified to direct their students to my networks demystified posts. I wanted to make sure that URL would continue to point to the version of the site they intended to link to, while also migrating the old content into a new system that I’d be able to update more fluidly. Thankfully the old directory for the site, /HIAL/ (the site used to be called History Is ALab), made that easier: the new version of the irregular would reside on scottbot.net, and the archive would remain on scottbot.net/HIAL/.
This apparently isn’t trivial. The first step was to use wget (explained and taught by Ian Milligan on the Programming Historian) to download a static version of the entire original irregular. After fiddling with the wget parameters and redownloading my site a few times, I ended up with a mostly-complete mirror of all the old content. Then I uploaded the entire mirror to my new host in the /HIAL/ directory. Yay!
The .htaccess file sits on your server, quietly directing traffic to the various places it should go. In my case, I needed to use regular expressions (remember that thing Shawn, Ian, and I taught in The Historian’s Macroscope?) to redirect all traffic pointing to HIAL/?[anything] to HIAL/index.html@[anything]. An hour or so of learning how .htaccess worked resulted in:
which, after some false starts, seems to work. The old site is now fixed, as in constant; secured; unwavering, at scottbot.net/HIAL/. The new irregular, at scottbot.net, is now fixed, as in functional, dynamic. It will continue to evolve and change.
the scottbot irregular is dead. Long live the scottbot irregular!
Nickoal Eichmann (corresponding author), Jeana Jorgensen, Scott B. Weingart1
NOTE: This is a pre-peer reviewed draft submitted for publication in Feminist Debates in Digital Humanities, eds. Jacque Wernimont and Elizabeth Losh, University of Minnesota Press (2017). Comments are welcome, and a downloadable dataset / more figures are forthcoming. This chapter will be released alongside another on the history of DH conferences, co-authored by Weingart & Eichmann (forthcoming), which will go into further detail on technical aspects of this study, including the data collection & statistics. Many of the materials first appeared on this blog. To cite this preprint, use the figshare DOI: https://dx.doi.org/10.6084/m9.figshare.3120610.v1
Digital Humanities (DH) is said to have a light side and a dark side. Niceness, globality, openness, and inclusivity sit at one side of this binary caricature; commodification, neoliberalism, techno-utopianism, and white male privilege sit at the other. At times, the plurality of DH embodies both descriptions.
We hope a diverse and critical DH is a goal shared by all. While DH, like the humanities writ large, is not a monolith, steps may be taken to improve its public face and shared values through positively influencing its communities. The Alliance of Digital Humanities Organizations’ (ADHO’s) annual conference hosts perhaps the largest such community. As an umbrella organization of six international digital humanities constituent organizations, as well as 200 DH centers in a few dozen countries, ADHO and its conference ought to represent the geographic, disciplinary, and demographic diversity of those who identify as digital humanists.
The annual conference offers insight into how the world sees DH. While it may not represent the plurality of views held by self-described digital humanists, the conference likely influences the values of its constituents. If the conference glorifies Open Access, that value will be taken up by its regular attendees; if the conference fails to prioritize diversity, this too will be reinforced.
This chapter explores fifteen years of DH conferences, presenting a quantified look at the values implicitly embedded in the event. Women are consistently underrepresented, in spite of the fact that the most prominent figures at the conference are as likely women as men. The geographic representation of authors has become more diverse over time—though authors with non-English names are still significantly less likely to pass peer review. The topical landscape is heavily gendered, suggesting a masculine bias may be built into the value system of the conference itself. Without data on skin color or ethnicity, we are unable to address racial or related diversity and bias here.
There have been some improvements over time and, especially recently, a growing awareness of diversity-related issues. While many of the conference’s negative traits are simply reflections of larger entrenched academic biases, this is no comfort when self-reinforcing biases foster a culture of microaggression and white male privilege. Rather than using this study as an excuse to write off DH as just another biased community, we offer statistics, critiques, and suggestions as a vehicle to improve ADHO’s conference, and through it the rest of self-identified Digital Humanities.
Digital humanities (DH), we are told, exists under a “big tent”, with porous borders, little gatekeeping, and, heck, everyone’s just plain “nice”. Indeed, the term itself is not used definitionally, but merely as a “tactical convenience” to get stuff done without worrying so much about traditional disciplinary barriers. DH is “global”, “public”, and diversely populated. It will “save the humanities” from its crippling self-reflection (cf. this essay), while simultaneously saving the computational social sciences from their uncritical approaches to data. DH contains its own mirror: it is both humanities done digitally, and the digital as scrutinized humanistically. As opposed to the staid, backwards-looking humanities we are used to, the digital humanities “experiments”, “plays”, and even “embraces failure” on ideological grounds. In short, we are the hero Gotham needs.
Digital Humanities, we are told, is a narrowly-defined excuse to push a “neoliberal agenda”, a group of “bullies” more interested in forcing humanists to code than in speaking truth to power. It is devoid of cultural criticism, and because of the way DHers uncritically adopt tools and methods from the tech industry, they in fact often reinforce pre-existing power structures. DH is nothing less than an unintentionally rightist vehicle for techno-utopianism, drawing from the same font as MOOCs and complicit in their devaluing of education, diversity, and academic labor. It is equally complicit in furthering both the surveillance state and the surveillance economy, exemplified in its stunning lack of response to the Snowden leaks. As a progeny of the computer sciences, digital humanities has inherited the same lack of gender and racial diversity, and any attempt to remedy the situation is met with incredible resistance.
The truth, as it so often does, lies somewhere in the middle of these extreme caricatures. It’s easy to ascribe attributes to Digital Humanities synecdochically, painting the whole with the same brush as one of its constituent parts. One would be forgiven, for example, for coming away from the annual international ADHO Digital Humanities conference assuming DH were a parade of white men quantifying literary text. An attendee of HASTAC, on the other hand, might leave seeing DH as a diverse community focused on pedagogy, but lacking in primary research. Similar straw-snapshots may be drawn from specific journals, subcommunities, regions, or organizations.
But these synecdoches have power. Our public face sets the course of DH, via who it entices to engage with us, how it informs policy agendas and funding allocations, and who gets inspired to be the next generation of digital humanists. Especially important is the constituency and presentation of the annual Digital Humanities conference. Every year, several hundred students, librarians, staff, faculty, industry professionals, administrators and researchers converge for the conference, organized by the Alliance of Digital Humanities Organizations (ADHO). As an umbrella organization of six international digital humanities constituent organizations, as well as 200 DH centers in a few dozen countries, ADHO and its conference ought to represent the geographic, disciplinary, and demographic diversity of those who identify as digital humanists. And as DH is a community that prides itself on its activism and its social/public goals, if the annual DH conference does not celebrate this diversity, the DH community may suffer a crisis of identity (…okay, a bigger crisis of identity).
So what does the DH conference look like, to an outsider? Is it diverse? What topics are covered? Where is it held? Who is participating, who is attending, and where are they coming from? This essay offers incomplete answers to these questions for fifteen years of DH conferences (2000-2015), focusing particularly on DH2013 (Nebraska, USA), DH2014 (Lausanne, Switzerland), and DH2015 (Sydney, Australia). 2 We do so with a double-agenda: (1) to call out the biases and lack of diversity at ADHO conferences in the earnest hope it will help improve future years’ conferences, and (2) to show that simplistic, reductive quantitative methods can be applied critically, and need not feed into techno-utopic fantasies or an unwavering acceptance of proxies as a direct line to Truth. By “distant reading” DH and turning our “macroscopes” on ourselves, we offer a critique of our culture, and hopefully inspire fruitful discomfort in DH practitioners who apply often-dehumanizing tools to their subjects, but have not themselves fallen under the same distant gaze.
Among other findings, we observe a large gender gap for authorship that is not mirrored among those who simply attend the conference. We also show a heavily gendered topical landscape, which likely contributes to topical biases during peer review. Geographic diversity has improved over fifteen years, suggesting ADHO’s strategy to expand beyond the customary North American / European rotation was a success. That said, there continues to be a visible bias against non-English names in the peer review process. We could not get data on ethnicity, race, or skin color, but given our regional and name data, as well as personal experience, we suspect in this area, diversity remains quite low.
We do notice some improvement over time and, especially in the last few years, a growing awareness of our own diversity problems. The #whatifDH2016 3 hashtag, for example, was a reaction to an all-male series of speakers introducing DH2015 in Sydney. The hashtag caught on and made it to ADHO’s committee on conferences, who will use it in planning future events. Our remarks here are in the spirit of #whatifDH2016; rather than using this study as an excuse to defame digital humanities, we hope it becomes a vehicle to improve ADHO’s conference, and through it the rest of our community.
Social Justice and Equality in the Digital Humanities
Diversity in the Academy
In order to contextualize gender and ethnicity in the DH community, we must take into account developments throughout higher education. This is especially important since much of DH work is done in university and other Ivory Tower settings. Clear progress has been made from the times when all-male, all-white colleges were the norm, but there are still concerns about the marginalization of scholars who are not white, male, able-bodied, heterosexual, or native English-speakers. Many campuses now have diversity offices and have set diversity-related goals at both the faculty and student levels (for example, see the Ohio State University’s diversity objectives and strategies 2007-12). On the digital front, blogs such as Conditionally Accepted, Fight the Tower, University of Venus, and more all work to expose the normative biases in academia through activist dialogue.
From both a historical and contemporary lens, there is data supporting the clustering of women and other minority scholars in certain realms of academia, from specific fields and subjects to contingent positions. When it comes to gender, the phrase “feminization” has been applied both to academia in general and to specific fields. It contains two important connotations: that of an area in which women are in the majority, and the sense of a change over time, such that numbers of women participants are increasing in relation to men (Leathwood and Read 2008, 10). It can also signal a less quantitative shift in values, “whereby ‘feminine’ values, concerns, and practices are seen to be changing the culture of an organization, a field of practice or society as a whole” (ibid).
In terms of specific disciplines, the feminization of academia has taken a particular shape. Historian Lynn Hunt suggests the following propositions about feminization in the humanities and history specifically: the feminization of history parallels what is happening in the social sciences and humanities more generally; the feminization of the social sciences and humanities is likely accompanied by a decline in status and resources; and other identity categories, such as ethnic minority status and age/generation, also interact with feminization in ways that are still becoming coherent.
Feminization has clear consequences for the perception and assignation of value of a given field. Hunt writes: “There is a clear correlation between relative pay and the proportion of women in a field; those academic fields that have attracted a relatively high proportion of women pay less on average than those that have not attracted women in the same numbers.” Thus, as we examine the topics that tend to be clustered by gender in DH conference submissions, we must keep in mind the potential correlations of feminization and value, though it is beyond the scope of this paper to engage in chicken-or-egg debates about the causal relationship between misogyny and the devaluing of women’s labor and women’s topics.
There is no obvious ethnicity-based parallel to the concept of the feminization of academia; it wouldn’t be culturally intelligible to talk about the “people-of-colorization of academia”, or the “non-white-ization of academia.” At any rate, according to a U.S. Department of Education survey, in 2013 79% of all full-time faculty in degree-granting postsecondary institutions were white. The increase of non-white faculty from 2009 (19.2% of the whole) to 2013 (21.5%) is very small indeed.
Why does this matter? As Jeffrey Milem, Mitchell Chang, and Anthony Lising Antonio write in regard to faculty of color, “Having a diverse faculty ensures that students see people of color in roles of authority and as role models or mentors. Faculty of color are also more likely than other faculty to include content related to diversity in their curricula and to utilize active learning and student-centered teaching techniques…a coherent and sustained faculty diversity initiative must exist if there is to be any progress in diversifying the faculty” (25). By centering marginalized voices, scholarly institutions have the ability to send messages about who is worthy of inclusion.
Recent Criticisms of Diversity in DH
In terms of DH specifically, diversity within the community and conferences has been on the radar for several years, and has recently gained special attention, as digital humanists and other academics alike have called for critical and feminist engagement in diversity and a move away from what seems to be an exclusionary culture. In January 2011, THATCamp SoCal included a section called “Diversity in DH,” in which participants explored the lack of openness in DH and, in the end, produced a document, “Toward an Open Digital Humanities” that summarized their discussions. The “Overview” in this document mirrors the same conversation we have had for the last several years:
We recognize that a wide diversity of people is necessary to make digital humanities function. As such, digital humanities must take active strides to include all the areas of study that comprise the humanities and must strive to include participants of diverse age, generation, sex, skill, race, ethnicity, sexuality, gender, ability, nationality, culture, discipline, areas of interest. Without open participation and broad outreach, the digital humanities movement limits its capacity for critical engagement. (ibid)
This proclamation represents the critiques of the DH landscape in 2011, in which DH practitioners and participants were assumed to be privileged and white, that they excluded student-learners, and that they held myopic views of what constitutes DH. Most importantly for this chapter, THATCamp SoCal’s “Diversity in DH” section participants called for critical approaches and social justice of DH scholarship and participation, including “principles for feminist/non-exclusionary groundrules in each session (e.g., ‘step up/step back’) so that the loudest/most entitled people don’t fill all the quiet moments.” They also advocated defending the least-heard voices “so that the largest number of people can benefit…”
These voices certainly didn’t fall flat. However, since THATCamps are often comprised of geographically local DH microcommunities, they benefit from an inclusive environment but suffer as isolated events. As result, it seems that the larger, discipline-specific venues which have greater attendance and attraction continue to amplify privileged voices. Even so, 2011 continued to represent a year that called for critical engagement in diversity in DH, with an explicit “Big Tent” theme for DH2011 held in Stanford, California. Embracing the concept the “Big Tent” deliberately opened the doors and widened the spectrum of DH, at least in terms of methods and approaches. However, as Melissa Terras pointed out, DH was “still a very rich, very western academic field” (Terras, 2011), even with a few DH2011 presentations engaging specifically with topics of diversity in DH. 4
A focus on diversity-related issues has only grown in the interim. We’ve recently seen greater attention and criticism of DH exclusionary culture, for instance, at the 2015 Modern Language Association (MLA) annual convention, which included the roundtable discussion “Disrupting Digital Humanities.” It confronted the “gatekeeping impulse” in DH, and echoing THATCamp SoCal 2011, these panelists aimed to shut down hierarchical dialogues in DH, encourage non-traditional scholarship, amplify “marginalized voices,” advocate for DH novices, and generously support the work of peers. 5 The theme for DH2015 in Sydney, Australia was “Global Digital Humanities,” and between its successes and collective action arising from frustrations at its failures, the community seems poised to pay even greater attention to diversity. Other recent initiatives in this vein worth mention include #dhpoco, GO::DH, and Jacqueline Wernimont’s “Build a Better Panel,” 6 whose activist goals are helping diversify the community and raise awareness of areas where the community can improve.
While it would be fruitful to conduct a longitudinal historiographical analysis of diversity in DH, more recent criticisms illustrate a history of perceived exclusionary culture, which is why we hope to provide a data-driven approach to continue the conversation and call for feminist and critical engagement and intervention.
While DH as a whole has been critiqued for its lack of diversity and inclusion, how does the annual ADHO DH conference measure up? To explore this in a data-driven fashion, we have gathered publicly available annual ADHO conference programs and schedules from 2000-2015. From those conference materials, we have entered presentation and author information into a spreadsheet to analyze various trends over time, such as gender and geography as indicators of diversity. Particular information that we have collected includes: presentation title, keywords (if available), abstract and full-text (if available), presentation type, author name, author institutional affiliation and academic department (if available), and corresponding country of that affiliation at the time of the presentation(s). We normalized and hand-cleaned names, institutions, and departments, so that, to the best of our knowledge, each author entry represented a unique person and, accordingly, was assigned a unique ID. Next, we added gender information (m/f/other/unknown) to authors by a combination of hand-entry and automated inference. While this is problematic for many reasons, 7 since it does not allow for diversity in gender options and tracing gender changes over time, it does give us a useful preliminary lense to view gender diversity at DH conferences.
For 2013’s conference, ADHO instituted a series of changes aimed at improving inclusivity, diversity, and quality. This drive was steered by that year’s program committee chair, Bethany Nowviskie, alongside 2014’s chair, Melissa Terras. Their reformative goals matched our current goals in this essay, and speak to a long history of experimentation and improvement efforts on behalf of ADHO. Their changes included making the conference more welcome to outsiders through ending policies that only insiders knew about; making the CFP less complex and easier to translate into multiple languages; taking reviewer language competencies into account systematically; and streamlining the submission and review process.
The biggest noticeable change to DH2013, however, was the institution of a reviewer bidding process and a phase of semi-open peer review. Peer reviewers were invited to read through and rank every submitted abstract according to how qualified they felt to review the abstract. Following this, the conference committee would match submissions to qualified peer reviewers, taking into account conflicts of interest. Submitting authors were invited to respond to reviews, and the committee would make a final decision based on the various reviews and rebuttals.This continues to be the process through DH2016. Changes continue to be made, most recently in 2016 with the addition of “Diversity” and “Multilinguality” as new keywords authors can append to their submissions.
While the list of submitted abstracts was private, accessible only to reviewers, as reviewers ourselves we had access to the submissions during the bidding phase. We used this access to create a dataset of conference submissions for DH2013, DH2014, and DH2015, which includes author names, affiliations, submission titles, author-selected topics, author-chosen keywords, and submission types (long paper, short paper, poster, panel).
We augmented this dataset by looking at the final conference programs in ‘13, ‘14, and ‘15, noting which submissions eventually made it onto the final conference program, and how they changed from the submission to the final product. This allows us to roughly estimate the acceptance rate of submissions, by comparing the submitted abstract lists to the final programs. It is not perfect, however, given that we don’t actually know whether submissions that didn’t make it to the final program were rejected, or if they were accepted and withdrawn. We also do not know who reviewed what, nor do we know the reviewers’ scores or any associated editorial decisions.
The original dataset, then, included fields for title, authors, author affiliations, original submission type, final accepted type, topics, keywords, and a boolean field for whether a submission made it to the final conference program. We cleaned the data up by merging duplicate people, ensuring e.g., if “Melissa Terras” was an author on two different submissions, she counted as the same person. For affiliations, we semi-automatically merged duplicate institutions, found the countries they reside in, and assigned those countries to broad UN regions. We also added data to the set, first automatically guessing a gender for each author, and then correcting the guesses by hand.
Given that abstracts were submitted to conferences with an expectation of privacy, we have not released the full submission dataset; we have, however, released the full dataset of final conference programs. 8
We would like to acknowledge the gross and problematic simplifications involved in this process of gendering authors without their consent or input. As Miriam Posner has pointed out, with regards to Getty’s Union List of Author Names, “no self-respecting humanities scholar would ever get away with such a crude representation of gender in traditional work”. And yet, we represent authors in just this crude fashion, labeling authors as male, female, or unknown/other. We did not encode changes of author gender over time, even though we know of at least a few authors in the dataset for whom this applies. We do not use the affordances of digital data to represent the fluidity of gender. This is problematic for a number of reasons, not least of which because, when we take a cookie cutter to the world, everything in the world will wind up looking like cookies.
We made this decision because, in the end, all data quality is contingent to the task at hand. It is possible to acknowledge an ontology’s shortcomings while still occasionally using that ontology to a positive effect. This is not always the case: often poor proxies get in the way a research agenda (e.g., citations as indicators of “impact” in digital humanities), rather than align with it. In the humanities, poor proxies are much more likely to get in the way of research than help it along, and afford the ability to make insensitive or reductivist decisions in the name of “scale”.
For example, in looking for ethnic diversity of a discipline, one might analyze last names as a proxy for country of origin, or analyze the color of recognized faces in pictures from recent conferences as a proxy for ethnic genealogy. Among other reasons, this approach falls short because ethnicity, race, and skin color are often not aligned, and last names (especially in the U.S.) are rarely indicative of anything at all. But they’re easy solutions, so people use them. These are moments when a bad proxy (and for human categories, proxies are almost universally bad) does not fruitfully contribute to a research agenda. As George E.P. Box put it, “all models are wrong, but some are useful.”
Some models are useful. Sometimes, the stars align and the easy solution is the best one for the question. If someone were researching immediate reactions of racial bias in the West, analyzing skin tone may get us something useful. In this case, the research focus is not someone’s racial identity, but someone’s race as immediately perceived by others, which would likely align with skin tone. Simply: if a person looks black, they’re more likely to be treated as such by the (white) world at large. 9
We believe our proxies, though grossly inaccurate, are useful for the questions of gender and geographic diversity and bias. The first step to improving DH conference diversity is noticing a problem; our data show that problem through staggeringly imbalanced regional and gender ratios. With regards to gender bias, showing whether reviewers are less likely to accept papers from authors who appear to be women can reveal entrenched biases, whether or not the author actually identifies as a woman. With that said, we invite future researchers to identify and expand on our admitted categorical errors, allowing everyone to see the contours of our community with even greater nuance.
The annual ADHO conference has grown significantly in the last fifteen years, as described in our companion piece 10, within which can be found a great discussion of our methods. This piece, rather than covering overall conference trends, focuses specifically on issues of diversity and acceptance rates. We cover geographic and gender diversity from 2000-2015, with additional discussions of topicality and peer review bias beginning in 2013.
Women comprise 36.1% of the 3,239 authors to DH conference presentations over the last fifteen years, counting every unique author only once. Melissa Terras’ names appears on 29 presentations between 200-2015, and Scott B. Weingart’s name appears on 4 presentations, but for the purpose of this metric each name counts only once. Female authorship representation fluctuates between 29%-38% depending on the year.
Weighting every authorship event individually (i.e., Weingart’s name counts 4 times, Terras’ 29 times), women’s representation drops to 32.7%. This reveals that women are less likely to author multiple pieces compared to their male counterparts. More than a third of the DH authorship pool are women, but fewer than a third of every name that appears on a presentation is a woman’s. Even fewer single-authored pieces are by a woman; only 29.8% of the 984 single-authored works between 2000-2015 female-authored. About a third (33.4%) of first authors on presentations are women. See Fig. 1 for a breakdown of these numbers over time. Note the lack of periodicity, suggesting gender representation is not affected by whether the conference is held in Europe or North America (until 2015, the conference alternated locations every year). The overall ratio wavers, but is neither improving nor worsening over time.
The gender disparity sparked controversy at DH2015 in Sydney. It was, however, at odds with a common anecdotal awareness that many of the most respected role-models and leaders in the community are women. To explore this disconnect, we experimented with using centrality in co-authorship networks as a proxy for fame, respectability, and general presence within the DH consciousness. We assume that individuals who author many presentations, co-author with many people, and play a central role in connecting DH’s disparate communities of authorship are the ones who are most likely to garner the respect (or at least awareness) of conference attendees.
We created a network of authors connected to their co-authors from presentations between 2000-2015, with ties strengthening the more frequently two authors collaborate. Of the 3,239 authors in our dataset, 61% (1,750 individuals) are reachable by one another via their co-authorship ties. For example, Beth Plale is reachable by Alan Liu because she co-authored with J. Stephen Downie, who co-authored with Geoffrey Rockwell, who co-authored with Alan Liu. Thus, 61% of the network is connected in one large component, and there are 299 smaller components, islands of co-authorship disconnected from the larger community.
The average woman co-authors with 5 other authors, and the average man co-authors with 5.3 other authors. The median number of co-authors for both men and women is 4. The average and median of several centrality measurements (closeness, betweenness, pagerank, and eigenvector) for both men and women are nearly equivalent; that is, any given woman is just as likely to be near the co-authorship core as any given man. Naturally, this does not imply that half of the most central authors are women, since only a third of the entire authorship pool are women. It means instead that gender does not influence one’s network centrality. Or at least it should.
The statistics show a curious trend for the most central figures in the network. Of the top 10 authors who co-author with the most others, 60% are women. Of the top 20, 45% are women. Of the top 50, 38% are women. Of the top 100, 32% are women. That is, the over half the DH co-authorship stars are women, but the further towards the periphery you look, the more men occupy the middle-tier positions (i.e., not stars, but still fairly active co-authors). The same holds true for the various centrality measurements: betweenness (60% women in top 10; 40% in top 20; 32% in top 50; 34% in top 100), pagerank (50% women in top 10; 40% in top 20; 32% in top 50; 28% in top 100), and eigenvector (60% women in top 10; 40% in top 20; 40% in top 50; 34% in top 100).
In short, half or more of the DH conference stars are women, but as you creep closer to the network periphery, you are increasingly likely to notice the prevailing gender disparity. This supports the mismatch between an anecdotal sense that women play a huge role in DH, and the data showing they are poorly represented at conferences. The results also match with the fact that women are disproportionately more likely to write about management and leadership, discussed at greater length below.
The heavily-male gender skew at DH conferences may lead one to suspect a bias in the peer review process. Recent data, however, show that if such a bias exists, it is not direct. Over the past three conferences, 71% of women and 73% of men who submitted presentations passed the peer review process. The difference is not great enough to rule out random chance (p=0.16 using χ²). The skew at conferences is more a result of fewer women submitting articles than of women’s articles not getting accepted. The one caveat, explained more below, is that certain topics women are more likely to write about are also less likely to be accepted through peer-review.
This does not imply a lack of bias in the DH community. For example, although only 33.5% of authors at DH2015 in Sydney were women, 46% of conference attendees were women. If women were simply uninterested in DH, the split in attendance vs. authorship would not be so high.
In regard to discussions of women in different roles in the DH community – less the publishing powerhouses and more the community leaders and organizers – the concept of the “glass cliff” can be useful. Research on the feminization of academia in Sweden uses the term “glass cliff” as a “metaphor used to describe a phenomenon when women are appointed to precarious leadership roles associated with an increased risk of negative consequences when a company is performing poorly and for example is experiencing profit falls, declining stock performance, and job cuts” (Peterson 2014, 4). The female academics (who also occupied senior managerial positions) interviewed in Helen Peterson’s study expressed concerns about increasing workloads, the precarity of their positions, and the potential for interpersonal conflict.
Institutional politics may also play a role in the gendered data here. Sarah Winslow says of institutional context that “female faculty are less likely to be located at research institutions or institutions that value research over teaching, both of which are associated with greater preference for research” (779). The research, teaching, and service divide in academia remains a thorny issue, especially given the prevalence of what has been called the pink collar workforce in academia, or the disproportionate amount of women working in low-paying teaching-oriented areas. This divide likely also contributed to differing gender ratios between attendees and authors at DH2015.
While the gendered implications of time allocation in universities are beyond the scope of this paper, it might be useful to note that there might be long-term consequences for how people spend their time interacting with scholarly tasks that extend beyond one specific institution. Winslow writes: “Since women bear a disproportionate responsibility for labor that is institution-specific (e.g., institutional housekeeping, mentoring individual students), their investments are less likely to be portable across institutions. This stands in stark contrast to men, whose investments in research make them more highly desirable candidates should they choose to leave their own institutions” (790). How this plays out specifically in the DH community remains to be seen, but the interdisciplinarity of DH along with its projects that span multiple working groups and institutions may unsettle some of the traditional bias that women in academia face.
Until 2015, the DH conference alternated every year between North America and Europe. As expected, until recently, the institutions represented at the conference have hailed mostly from these areas, with the primary locus falling in North America. In fact, since 2000, North American authors were the largest authorial constituency at eleven of the fifteen conferences, even though North America only hosted the conference seven times in that period.
With that said, as opposed to gender representation, national and institutional diversity is improving over time. Using an Index of Qualitative Variation (IQV), institutional variation begins around 0.992 in 2000 and ends around 0.996 in 2015, with steady increases over time. National IQV begins around 0.79 in 2010 and ends around 0.83 in 2015, also with steady increases over time. The most recent conference was the first that included over 30% of authors and attendees arriving from outside Europe or North America. Now that ADHO has implemented a three-year cycle, with every third year marked by a movement outside its usual territory, that diversity is likely to increase further still.
The most well-represented institutions are not as dominating as some may expect, given the common view of DH as a community centered around particular powerhouse departments or universities. The university with the most authors contributing to DH conferences (2.4% of the total authors) is King’s College London, followed by the Universities of Illinois (1.85%), Alberta (1.83%), and Virginia (1.75%). The most prominent university outside of North America or Europe is Ritsumeikan University, contributing 1.07% of all DH conference authors. In all, over a thousand institutions have contributed authors to the conference, and that number increases every year.
While these numbers represent institutional origins, the data available does not allow any further diving into birth countries, native language, ethnic identities, etc. The 2013-2015 dataset, including peer review information, does yield some insight into geography-influenced biases that may map to language or identity. While the peer review data do not show any clear bias by institutional country, there is a very clear bias against names which do not appear frequently in the U.S. Census or Social Security Index. We discovered this when attempting to statistically infer the gender of authors using these U.S.-based indices. 11 From 2013-2015, presentations written by those with names appearing frequently in these indices were significantly more likely to be accepted than those written by authors with non-English names (p < 0.0001). Whereas approximately 72% of authors with common U.S. names passed peer review, only 61% of authors with uncommon names passed. Without more data, we have no idea whether this tremendous disparity is due to a bias against popular topics from non-English-speaking countries, a higher likelihood of peer reviewers rejecting text written by non-native writers, an implicit bias by peer reviewers when they see “foreign” names, or something else entirely.
When submitting a presentation, authors are given the opportunity to provide keywords for their submission. Some keywords can be chosen freely, while others must be chosen from a controlled list of about 100 potential topics. These controlled keywords are used to help in the process of conference organization and peer reviewer selection, and they stay roughly constant every year. New keywords are occasionally added to the list, as in 2016, where authors can now select three topics which were not previously available: “Digital Humanities – Diversity”, “Digital Humanities – Multilinguality”, and “3D Printing”. The 2000-2015 conference dataset does not include keywords for every article, so this analysis will only cover the more detailed dataset, 2013-2015, with additional data on submissions for DH2016.
From 2013-2016, presentations were tagged with an average of six controlled keywords per submission. The most-used keywords are unsurprising: “Text Analysis” (tagged on 22% of submissions), “Data Mining / Text Mining” (20%), “Literary Studies” (20%), “Archives, Repositories, Sustainability And Preservation” (19%), and “Historical Studies” (18%). The most frequently-used keyword potentially pertaining directly to issues of diversity, “Cultural Studies”, appears on on 14% of submissions from 2013-2016. Only 2% of submissions are tagged with “Gender Studies”. The two diversity-related keywords introduced this year are already being used surprisingly frequently, with 9% of submissions in 2016 tagged “Digital Humanities – Diversity” and 6% of submissions tagged “Digital Humanities – Multilinguality”. With over 650 conference submissions for 2016, this translates to a reasonably large community of DH authors presenting on topics related to diversity.
Joining the topic and gender data for 2013-2015 reveals the extent to which certain subject matters are gendered at DH conferences. 12 Women are twice as likely to use the “Gender Studies” tag as male authors, whereas men are twice as likely to use the “Asian Studies” tag as female authors. Subjects related to pedagogy, creative / performing arts, art history, cultural studies, GLAM (galleries, libraries, archives, museums), DH institutional support, and project design/organization/management are more likely to be presented by women. Men, on the other hand, are more likely to write about standards & interoperability, the history of DH, programming, scholarly editing, stylistics, linguistics, network analysis, and natural language processing / text analysis. It seems DH topics have inherited the usual gender skews associated with the disciplines in which those topics originate.
We showed earlier that there was no direct gender bias in the peer review process. While true, there appears to be indirect bias with respect to how certain gendered topics are considered acceptable by the DH conference peer reviewers. A woman has just as much chance of getting a paper through peer review as a man if they both submit a presentation on the same topic (e.g., both women and men have a 72% chance of passing peer review if they write about network analysis, or a 65% chance of passing peer review if they write about knowledge representation), but topics that are heavily gendered towards women are less likely to get accepted. Cultural studies has a 57% acceptance rate, gender studies 60%, pedagogy 51%. Male-skewed topics have higher acceptance rates, like text analysis (83%), programming (80%), or Asian studies (79%). The female-gendering of DH institutional support and project organization also supports our earlier claim that, while women are well-represented among the DH leadership, they are more poorly represented in those topics that the majority of authors are discussing (programming, text analysis, etc.).
Regarding the clustering – and devaluing – of topics that women tend to present on at DH conferences, the widespread acknowledgement of the devaluing of women’s labor may help to explain this. We discussed the feminization of academia above, and indeed, this is a trend seen in practically all facets of society. The addition of emotional labor or caretaking tasks complicates this. Economist Teresa Ghilarducchi explains: “a lot of what women do in their lives is punctuated by time outside of the labor market — taking care of family, taking care of children — and women’s labor has always been devalued…[people] assume that she had some time out of the labor market and that she was doing something that was basically worthless, because she wasn’t being paid for it.” In academia specifically, the labyrinthine relationship of pay to tasks/labor further obscures value: we are rarely paid per task (per paper published or presented) on the research front; service work is almost entirely invisible; and teaching factors in with course loads, often with more up-front transparency for contingent laborers such as adjuncts and part-timers.
Our results seem to point to less of an obvious bias against women scholars than a subtler bias against topics that women tend to gravitate toward, or are seen as gravitating toward. This is in line with the concept of postfeminism, or the notion that feminism has met its main goals (e.g. getting women the right to vote and the right to an education), and thus is irrelevant to contemporary social needs and discourse. Thoroughly enmeshed in neoliberal discourse, postfeminism makes discussing misogyny seem obsolete and obscures the subtler ways in which sexism operates in daily life (Pomerantz, Raby, and Stefanik 2013). While individuals may or may not choose to identify as postfeminist, the overarching beliefs associated with postfeminism have permeated North American culture at a number of levels, leading us to posit the acceptance of the ideals of postfeminism as one explanation for the devaluing of topics that seem associated with women.
Discussion and Future Research
The analysis reveals an annual DH conference with a growing awareness of diversity-related issues, with moderate improvements in regional diversity, stagnation in gender diversity, and unknown (but anecdotally poor) diversity with regards to language, ethnicity, and skin color. Knowledge at the DH conference is heavily gendered, though women are not directly biased against during peer review, and while several prominent women occupy the community’s core, women occupy less space in the much larger periphery. No single or small set of institutions dominate the conference attendance, and though North America’s influence on ADHO cannot be understated, recent ADHO efforts are significantly improving the geographic spread of its constituency.
The DH conference, and by extension ADHO, is not the digital humanities. It is, however, the largest annual gathering of self-identified digital humanists, 13 and as such its makeup holds influence over the community at large. Its priorities, successes, and failures reflect on DH, both within the community and to the outside world, and those priorities get reinforced in future generations. If the DH conference remains as it is—devaluing knowledge associated with femininity, comprising only 36% women, and rejecting presentations by authors with non-English names—it will have significant difficulty attracting a more diverse crowd without explicit interventions. Given the shortcomings revealed in the data above, we present some possible interventions that can be made by ADHO or its members to foster a more diverse community, inspired by #WhatIfDH2016:
As pointed out by Yvonne Perkins, Ask presenters to include a brief “Collections Used” section, when appropriate. Such a practice would highlight and credit the important work being done by those who aren’t necessarily engaging in publishable research, and help legitimize that work to conference attendees.
As pointed out by Vika Zafrin, create guidelines for reviewers explicitly addressing diversity, and provide guidance on noticing and reducing peer review bias.
As pointed out by Vika Zafrin, community members can make an effort to solicit presentation submissions from women and people of color.
As pointed out by Vika Zafrin, collect and analyze data on who is peer reviewing, to see whether or the extent to which biases creep in at that stage.
As pointed out by Aimée Morrison, ensure that the conference stage is at least as diverse as the conference audience. This can be accomplished in a number of ways, from conference organizers making sure their keynote speakers draw from a broad pool, to organizing last-minute lightning lectures specifically for those who are registered but not presenting.
As pointed out by Christina Boyles, encourage the submission of research focused around the intersection of race, gender, and sexuality studies. This may be partially accomplished by including more topical categories for conference submissions, a step which ADHO has already taken for 2016.
As pointed out by many, take explicit steps in ensuring conference access to those with disabilities. We suggest this become an explicit part of the application package submitted by potential host institutions.
As pointed out by many, ensure the ease of participation-at-a-distance (both as audience and as speaker) for those without the resources to travel.
Give marginalized communities greater representation in the DH Conference peer reviewer pool. This can be done grassroots, with each of us reaching out to colleagues to volunteer as reviewers, and organizationally, perhaps by ADHO creating a volunteer group to seek out and encourage more diverse reviewers.
Consider the difference between diversifying (verb) vs. talking about diversity (noun), and consider whether other modes of disrupting hegemony, such as decolonization and queering, might be useful in these processes.
Contribute to the #whatifDH2016 and #whatifDH2017 discussions on twitter with other ideas for improvements.
Many options are available to improve representation at DH conferences, and some encouraging steps are already being taken by ADHO and its members. We hope to hear more concrete steps that may be taken, especially learned from experiences in other communities or outside of academia, in order to foster a healthier and more welcoming conference going forward.
In the interest of furthering these goals and improving the organizational memory of ADHO, the public portion of the data (final conference programs with full text and unique author IDs) is available alongside this publication [will link in final draft]. With this, others may test, correct, or improve our work. We will continue work by extending the dataset back to 1990, continuing to collect for future conferences, and creating an infrastructure that will allow the database to connect to others with similar collections. This will include the ability to encode more nuanced and fluid gender representations, and for authors to correct their own entries. Further work will also include exploring topical co-occurrence, institutional bias in peer review, how institutions affect centrality in the co-authorship network, and how authors who move between institutions affect all these dynamics.
The Digital Humanities will never be perfect. It embodies the worst of its criticisms and the best of its ideals, sometimes simultaneously. We believe a more diverse community will help tip those scales in the right direction, and present this chapter in service of that belief.
Peterson, Helen. “An Academic ‘Glass Cliff’? Exploring the Increase of Women in Swedish Higher Education Management.” Athens Journal of Education 1, no. 1 (February 2014): 32–44.
Pomerantz, Shauna, Rebecca Raby, and Andrea Stefanik. “Girls Run the World? Caught between Sexism and Postfeminism in the School.” *Gender & Society *27, no. 2 (April 1, 2013): 185-207. doi:10.1177/0891243212473199
Each author contributed equally to the final piece; please disregard authorship order. ↩
See Melissa Terras, “Disciplined: Using Educational Studies to Analyse ‘Humanities Computing.’” Literary and Linguistic Computing 21, no. 2 (June 1, 2006): 229–46. doi:10.1093/llc/fql022. Terras takes a similar approach, analyzing Humanities Computing “through its community, research, curriculum, teaching programmes, and the message they deliver, either consciously or unconsciously, about the scope of the discipline.” ↩
The authors have created a browsable archive of #whatifDH2016 tweets. ↩
Of the 146 presentations at DH2011, two standout in relation to diversity in DH: “Is There Anybody out There? Discovering New DH Practitioners in other Countries” and “A Trip Around the World: Balancing Geographical Diversity in Academic Research Teams.” ↩
See “Disrupting DH,” http://www.disruptingdh.com/ ↩
See Wernimont’s blog post, “No More Excuses” (September 2015) for more, as well as the Tumblr blog, “Congrats, you have an all male panel!” ↩
Miriam Posner offers a longer and more eloquent discussion of this in, “What’s Next: The Radical, Unrealized Potential of Digital Humanities.” Miriam Posner’s Blog. July 27, 2015. http://miriamposner.com/blog/whats-next-the-radical-unrealized-potential-of-digital-humanities/ ↩
[Link to the full public dataset, forthcoming and will be made available by time of publication]) ↩
We would like to acknowledge that race and ethnicity are frequently used interchangeably, though both are cultural constructs with their roots in Darwinian thought, colonialism, and imperialism. We retain these terms because they express cultural realities and lived experiences of oppression and bias, not because there is any scientific validity to their existence. For more on this tension, see John W.Burton, (2001), Culture and the Human Body: An Anthropological Perspective. Prospect Heights, Illinois: Waveland Press, 51-54. ↩
Weingart, S.B. & Eichmann, N. (2016). “What’s Under the Big Tent?: A Study of ADHO Conference Abstracts.” Manuscript submitted for publication. ↩
We used the process and script described in: Lincoln Mullen (2015). gender: Predict Gender from Names Using Historical Data. R package version 0.5.0.9000 (https://github.com/ropensci/gender) and Cameron Blevins and Lincoln Mullen, “Jane, John … Leslie? A Historical Method for Algorithmic Gender Prediction,” Digital Humanities Quarterly 9.3 (2015). ↩
For a breakdown of specific numbers of gender representation across all 96 topics from 2013-2015, see Weingart’s “Acceptances to Digital Humanities 2015 (part 4)”. ↩
While ADHO’s annual conference is usually the largest annual gathering of digital humanists, that place is constantly being vied for by the Digital Humanities Summer Institute in Victoria, Canada, which in 2013 boasted more attendees than DH2013 in Lincoln, Nebraska. ↩
[note: originally published as draft on March 17th, 2016]
DH2016 announced their final(ish) program yesterday and, of course, that means it’s analysis time. Every year, I steal scrape submission data from the reviewer interface, and then scrape the final conference program, to report acceptance rates and basic stats for the annual event. See my previous 7.2 million previous posts on the subject. Nobody gives me data, I take it (capta, amiright?), so take these results with as many grains of salt as you’ll find at the DH2016 salt mines.
As expected, this will be the biggest ADHO conference to date, continuing a mostly-consistent trend of yearly growth. Excluding workshops & keynotes, this year’s ADHO conference in Kraków, Poland will feature 417 posters & presentations, up from 259 in 2015 (an outlier, held in Australia) and the previous record of 345 in 2014 (Switzerland). At this rate, the number of DH presentations should surpass our human population by the year 2126 (or earlier in the case of unexpected zombies).
Acceptance rates this year are on par with previous years. An email from ADHO claims this year’s overall acceptance rate to be 62%, and my calculations put it at 64%. Previous years were within this range: 2013 Nebraska (64%), 2014 Switzerland (59%), and 2015 Australia (72%). Regarding form, the most difficult type of presentation to get through review is a long paper, with only 44% of submitted long papers being accepted as long papers. Another 7.5% of long papers were accepted as posters, and 10% as short papers. In total, 62% of long paper submissions were accepted in some form. Reviewers accepted 75% of panels and posters, leaving them mostly in their original form. The category least likely to get accepted in any form was the short paper, with an overall 59% acceptance rate (50% accepted as short papers; 8% accepted as posters). The moral of the story is that your best bet to get accepted to DH is to submit a poster. If you hate posters, submit a long paper; even if it’s not accepted as a long paper, it might still get in as a short or a poster. But if you do hate posters, maybe just avoid this conference.
About a third of this year’s presentations are single-authored, another third dual-authored, and the last third are authored by three or more people. As with 2013-2015, more authors means a more likely acceptance: reviewers accepted 51% of single-authored presentations, 66% of dual-authored presentations, and 74% of three-or-more-authored presentations.
Topically, the landscape of DH2016 will surprise few. A quarter of all presentations will involve text analysis, followed by historical studies (23% of all presentations), archives (21%), visualizations (20%), text/data mining (20%), and literary studies (20%). DH self-reflection is always popular, with this year’s hot-button issues being DH diversity (10%), DH pedagogy (10%), and DH facilities (7%). Surprisingly, other categories pertaining to pedagogy are also growing compared to previous years, though mostly it’s due to more submissions in that area. Reviewers still don’t rate pedagogy presentations very highly, but more on that in the next post. Some topical low spots compared to previous years include social media (2% of all presentations), anthropology (3%), VR/AR (3%), crowdsourcing (4%), and philosophy (5%).
This year will likely be the most linguistically diverse conference thus-far: 92% English, 7% French, 0.5% German, with other presentations in Spanish, Italian, Polish, etc. (And by “most linguistically diverse” obviously I mean “really not very diverse but have you seen the previous conferences?”) Submitting in a non-English language doesn’t appreciably affect acceptance rates.
That’s all for now. Stay-tuned for Pt. 2, with more thorough comparisons to previous years, actual granular data/viz on topics, analyses of gender and geography, as well as interpretations of what the changing landscape means for DH.
Twice a year I indulge my meta-disciplinary sweet tooth: once to look at who’s submitting what to ADHO’s annual digital humanities conference, and once to look at which pieces get accepted (see the rest of the series). This post presents my first look at DH2016 conference submissions, the data for which I scraped from ConfTool during the open peer review bidding phase. Open peer review bidding began in 2013, so I have 4 years of data. I opt not to publish this data, as most authors submit pieces under an expectation of privacy, and might violently throw things at my face if people find out which submissions weren’t accepted. Also ethics.
Submission Numbers & Types
The basic numbers: 652 submissions (268 long papers, 223 short papers, 33 panels / multiple paper sessions, 128 posters). For those playing along at home, that’s:
2013 Nebraska: 348 (144/118/20/66)
2014 Lausanne: 589 (250/198/30/111)
2015 Sydney: 360 (192/102/13/53)
2016 Kraków: 652 (268/223/33/128)
DH2016 submissions are on par to continue the consistent-ish trend of growth every year since 1999, the large dip in 2015 unsurprising given its very different author pool, and the fact that it was the first time the conference visited the southern hemisphere or Asia-Pacific. The different author pool in 2015 also likely explains why it was the only conference to deviate from the normal submission-type ratios.
Regarding co-authorship, the number has shifted this year, though not enough to pass any significance tests.
DH2016 has proportionally slightly fewer single authored papers than previous years, and slightly more 2-, 3-, and 4-authored papers. One submission has 17 authors (not quite the 5,154-author record of high energy physics, but we’re getting there, eh?), but mostly it’s par for the course here.
Topically, DH2016 submissions continue many trends seen previously.
Authors must tag their submissions into multiple categories, or topics, using a controlled vocabulary. The figure presents a list of topics tagged to submissions, ordered top-to-bottom by the largest proportion of submissions with a certain tag for 2016. Nearly 25% of DH2016 submissions, for example, were tagged with “Text Analysis”. The dashed lines represent previous years’ tag proportions, with the darkest representing 2015, getting lighter towards 2013. New topics, those which just entered the controlled vocabulary this year, are listed in red. They are 3D Printing, DH Multilinguality, and DH Diversity.
Scroll past the long figure below to read my analysis:
In a reveal that will shock all species in the known universe, text analysis dominates DH2016 submissions—the proportion even grew from previous years. Text & data mining, archives, and data visualization aren’t far behind, each growing from previous years.
What did actually (pleasantly) surprise me was that, for the first time since I began counting in 2013, history submissions outnumber literary ones. Compare this to 2013, when literary studies were twice as well represented as historical. Other top-level categories experiencing growth include: corpus studies, content analysis, knowledge representation, NLP, and linguistics.
Two areas which I’ve pointed out previously as needing better representation, geography and pedagogy, both grew compared to previous years. I’ve also pointed out a lack of discussion of diversity, but part of that lack was that authors had no “diversity” category to label their research with—that is, the issue I pointed out may have been as much a problem with the topic taxonomy as with the research itself. ADHO added “Diversity” and “Multilinguality” as potential topic labels this year, which were tagged to 9.4% and 6.5% of submissions, respectively. One-in-ten submissions dealing specifically with issues of diversity is encouraging to see.
Unsurprisingly, since Sydney, submissions tagged “Asian Studies” have dropped. Other consistent drops over the last few years include software design, A/V & multimedia (sadface), information retrieval, XML & text encoding, internet & social media-related topics, crowdsourcing, and anthropology. The conference is also getting less self-referential, with a consistent drop in DH histories and meta-analyses (like this one!). Mysteriously, submissions tagged with the category “Other” have dropped rapidly each year, suggesting… dunno, aliens?
I have the suspicion that some numbers are artificially growing because there are more topics tagged per article this year than previous years, which I’ll check and report on in the next post.
It may be while before I upload the next section due to other commitments. In the meantime, you can fill your copious free-time reading earlier posts on this subject or my recent book with Shawn Graham & Ian Milligan, The Historian’s Macroscope. Maybe you can buy it for your toddler this holiday season. It fits perfectly in any stocking (assuming your stockings are infinitely deep, like Mary Poppins’ purse, which as a Jew watching Christmas from afar I just always assume is the case).
I’ve had a blast since starting as Digital Humanities Specialist at CMU. Enough administrators, faculty, and students are on board to make building a DH strength here pretty easy, and we’re neighbors to Pitt DHRX, a really supportive supercomputing center, and great allies in the Mayor’s Office keen on a city rich with art, data, and both combined.
We want a developer to help jump-start our research efforts. You’ll be working as a full collaborator on projects from all sorts of domains, and as a review board member you’ll have a strong say in which projects they are and how they get implemented. You and I will work together in achievable rapid prototyping, analyzing data, and web deployment.
The idea is we build or do stuff that’s scholarly, interesting, and can have a proof-of-concept or article done in a semester or two. With that, the project can go on to seek additional funding and a full-time specialized programmer, or we can finish there and all be proud authors or creators of something we enjoyed making.
Ideally, you have a social science, humanities, journalism, or similar research background, and the broad tech chops to create a d3 viz, DeepDream some dogs into a work of art, manage a NoSQL database, and whatever else seems handy. Ruby on Rails, probably.
We’re looking for someone who loves playing with new tech stacks, isn’t afraid to get their hands dirty, and knows how to talk to humans. You probably have a static site and a github account. You get excited by interactive data stories, and want to make them with us. This job values breadth over depth and done over perfect.
The job isn’t as insane as it sounds—you don’t actually need to be able to do all this already, just be the sort of person who can learn on the fly. A bachelor’s degree or similar experience is required, with a strong preference for candidates with some research background. You’ll need to submit or point to some examples of work you’ve done.
We’re an equal opportunity employer, and would love to see applications from women, minorities, or other groups who often have a tough time getting developer jobs. If you work here you can take two free classes a semester. Say, who wants a fancy CMU computer science graduate degree? We can offer an awesome city, friendly coworkers, and a competitive salary (also Pittsburgh’s cheap so you wouldn’t live in a closet, like in SF or NYC).
The ad, if you’re too lazy to click the link, or are scared CMU hosts viruses:
Digital Humanities Developer, Dietrich College of Humanities and Social Sciences
Summary The Dietrich College of Humanities and Social Sciences at Carnegie Mellon University (CMU) is undertaking a long-term initiative to foster digital humanities research among its faculty, staff, and students. As part of this initiative, CMU seeks an experienced Developer to collaborate on cutting edge interdisciplinary projects.
CMU is a world leader in technology-oriented research, and a highly supportive environment for cross-departmental teams. The Developer would work alongside researchers from Dietrich and elsewhere to plan and implement digital humanities projects, from statistical analyses of millions of legal documents to websites that crowdsource grammars of endangered languages. Located in the the Office of The Dean under CMU’s Digital Humanities Specialist, the developer will help start up faculty projects into functioning prototypes where they can acquire sustaining funding to hire specialists for more focused development.
The position emphasizes rapid, iterative deployment and the ability to learn new techniques on the job, with a focus on technologies intersecting data science and web development, such as D3.js, NoSQL, Shiny (R), IPython Notebooks, APIs, and Ruby on Rails. Experience with digital humanities or computational social sciences is also beneficial, including work with machine learning, GIS, or computational linguistics.
The individual in this position will work with clients and the digital humanities specialist to determine achievable short-term prototypes in web development or data analysis/presentation, and will be responsible for implementing the technical aspects of these goals in a timely fashion. As a collaborator, the Digital Humanities Developer will play a role in project decision-making, where appropriate, and will be credited on final products to which they extensively contribute.
Please submit a cover letter, phone numbers and email addresses for two references, a résumé or cv, and a page describing how your previous work fits the job, including links to your github account or other relevant previous work examples.
Bachelor’s Degree in humanities computing, digital humanities, informatics, computer science, related field, or equivalent combination of training and experience.
At least one year of experience in modern web development and/or data science, preferably in a research and development team setting.
Some system administration.
Advanced degree in digital humanities, computational social science, informatics, or data science. Coursework in data visualization, machine learning, statistics, or MVC web applications.
Three or more years at the intersection of web development/deployment and machine learning (e.g. data journalism or digital humanities) in an agile software environment.
Ability to assess client needs and offer creative research or publication solutions.
Any combination of GIS, NLTK, statistical models, ABMs, web scraping, mahout/hadoop, network analysis, data visualization, RESTful services, testing frameworks, XML, HPC.
Job Function: Research Programming
Primary Location: United States-Pennsylvania-Pittsburgh
Time Type: Full Time
Organization: DIETRICH DEAN’S OFFICE
Minimum Education Level: Bachelor’s Degree or equivalent
Musings on materiality and cost after a tour of The Shoah Foundation.
Forgetting The Holocaust
As the only historian in my immediate family, I’m responsible for our genealogy, saved in a massive GEDCOM file. Through the wonders of the web, I now manage quite the sprawling tree: over 100,000 people, hundreds of photos, thousands of census records & historical documents. The majority came from distant relations managing their own trees, with whom I share.
Such a massive well-kept dataset is catnip for a digital humanist. I can analyze my family! The obvious first step is basic stats, like the most common last name (Aber), average number of kids (2), average age at death (56), or most-frequently named location (New York). As an American Jew, I wasn’t shocked to see New York as the most-common place name in the list. But I was unprepared for the second-most-common named location: Auschwitz.
I’m lucky enough to write this because my great grandparents all left Europe before 1915. My grandparents don’t have tattoos on their arms or horror stories about concentration camps, though I’ve met survivors their age. I never felt so connected to The Holocaust, HaShoah, until I took time to see explore the hundreds of branches of my family tree that simply stopped growing in the 1940s.
I grew up hearing “Never Forget” as a mantra to honor the 11 million victims of hate and murder at the hands of Nazis, and to ensure it never happens again. That a Canadian official has forgotten—that we have all forgotten many of the other genocides that haunt human history—suggests how easy it is to forget. And how much work it is to remember.
The material cost of remembering 50,000 Holocaust survivors & witnesses
Yad Vashem (“a place and a name”) represents the attempt to inscribe, preserve, and publicize the names of Jewish Holocaust victims who have no-one to remember them. Over four million names have been collected to date.
The USC Shoah Foundation, founded by Steven Spielberg in 1994 to remember Holocaust survivors and witnesses, is both smaller and larger than Yad Vashem. Smaller because the number of survivors and witnesses still alive in 1994 numbered far fewer than Yad Vashem‘s 4.3 million; larger because the foundation conducted video interviews: 100,000 hours of testimony from 50,000 individuals, plus recent additions of witnesses and survivors of other genocides around the world. Where Yad Vashem remembers those killed, the Shoah Foundation remembers those who survived. What does it take to preserve the memories of 50,000 people?
I got a taste of the answer to that question at a workshop this week hosted by USC’s Digital Humanities Program, who were kind enough to give us a tour of the Shoah Foundation facilities. Sam Gustman, the foundation’s CTO and Associate Dean of USC’s Libraries, gave the tour.
Digital preservation it a complex process. In this case, it began by digitizing 235,000 analog Betacam SP Videocassettes, on which the original interviews had been recorded, a process which took from 2008-2012. This had to be done quickly (automatically/robotically), given that cassette tapes are prone to become sticky, brittle, and unplayable within a few decades due to hydrolysis. They digitized about 30,000 hours per year. The process eventually produced 8 petabytes (link to more technical details) of lossless JPEG 2000 videos, roughly the equivalent of 2 million DVDs. Stacked on top of each other, those DVDs would reach three times higher than Burj Khalifa, the world’s tallest tower.
From there, the team spent quite some time correcting errors that existed in the original tapes, and ones that were introduced in the process of digitization. They employed a small army of signal processing students, patented new technologies for automated error detection & processing/cleaning, and wound up cleaning video from about 12,000 tapes. According to our tour guide, cleaning is still happening.
Lest you feel safe knowing that digitization lengthens the preservation time, turns out you’re wrong. Film lasts longer than most electronic storage, but making film copies would have cost the foundation $140,000,000 and made access incredibly difficult. Digital copies would only cost tens of millions of dollars, even though hard-drives couldn’t be trusted to last more than a decade. Their solution was a RAID hard-drive system in an Oracle StorageTek SL8500 (of which they have two), and a nightly process of checking video files for even the slightest of errors. If an error is found, a backup is loaded to a new cartridge, and the old cartridge is destroyed. Their two StorageTeks each fit over 10,000 drive cartridges, have 55 petabytes worth of storage space, weigh about 4,000 lbs, and are about the size of a New York City apartment. If a drive isn’t backed up and replaced within three years, they throw it out and replace it anyway, just in case. And this setup apparently saved the Shoah Foundation $6 million.
Oh, and they have another facility a few states away, connected directly via high-bandwidth fiber optic cables, where everything just described is duplicated in case California falls into the ocean.
So how much does it cost to remember 50,000 Holocaust witnesses and survivors for, say, 20 years? I mean, above and beyond the cost of building a cutting edge facility, developing new technologies of preservation, cooling and housing a freight container worth of hard drives, laying fiber optic cables below ground across several states, etc.? I don’t know. But I do know how much the Shoah Foundation would charge you to save 8 petabytes worth of videos for 20 years, if you were a USC Professor. They’d charge you $1,000/TB/20 years.
The Foundation’s videos take up 8,000 terabytes, which at $1,000 each would cost you $8 million per 20 years, or about half a million dollars per year. Combine that with all the physical space it takes up, and never forgetting the Holocaust is sounding rather prohibitive. And what about after 20 years, when modern operating systems forget how to read JPEG 2000 or interface with StorageTek T10000C Tape Drives, and the Shoah Foundation needs to undertake another massive data conversion? I can see why that Canadian official didn’t manage it.
The Reconcentration of Holocaust Survivors
While I appreciated the guided tour of the exhibit, and am thankful for the massive amounts of money, time, and effort scholars and donors are putting into remembering Holocaust survivors, I couldn’t help but be creeped out by the experience.
Our tour began by entering a high security facility. We signed our names on little pieces of paper and were herded through several layers of locked doors and small rooms. Not quite the way one expects to enter the project tasked with remembering and respecting the victims of genocide.
The Nazi’s assembly-line techniques for mass extermination led to starkly regular camps, like Auschwitz pictured above, laid out in efficient grids for the purpose of efficient control and killings. “Concentration camp”, by the way, refers to the concentration of people into small spaces, coming from “reconcentration camps” in Cuba. Now we’re concentrating 50,000 testimonies into a couple of closets with production line efficiency, reconcentrating the stories of people who dispersed across the world, so they’re all in one easy-to-access place.
We’ve squeezed 100,000 hours of testimony into a server farm that consists of a series of boxes embedded in a series of larger boxes, all aligned to a grid; input, output, and eventual destruction of inferior entities handled by robots. Audits occur nightly.
The Shoah Foundation materials were collected, developed, and preserved with the utmost respect. The goal is just, the cause respectable, and the efforts incredibly important. And by reconcentrating survivors’ stories, they can now be accessed by the world. I don’t blame the Foundation for the parallels which are as much a construct of my mind as they are of the society in which this technology developed. Still, on Halloween, it’s hard to avoid reflecting on the material, monetary, and ultimately dehumanizing costs of processing ghosts into the machine.
This is the incredibly belated transcript of my HASTAC 2015 keynote. Many thanks to the organizers for inviting me, and to my fellow participants for all the wonderful discussions. The video and slides are also online. You can find citations to some of the historical illustrations and many of my intellectual inspirations here. What I said and what I wrote probably don’t align perfectly.
If you take a second to expand and disentangle “HASTAC”, you get a name of an organization that doubles as a fairly strong claim about the world: that Humanities, Arts, Science, and Technology are separate things, that they probably aren’t currently in alliance with one another, and that they ought to form an alliance.
This intention is reinforced in the theme of this year’s conference: “The Art and Science of Digital Humanities.” Here again we get the four pillars: humanities, arts, science, and technology. In fact, bear with me as I read from the CFP:
We welcome sessions that address, exemplify, and interrogate the interdisciplinary nature of DH work. HASTAC 2015 challenges participants to consider how the interplay of science, technology, social sciences, humanities, and arts are producing new forms of knowledge, disrupting older forms, challenging or reifying power relationships, among other possibilities.
Here again is that implicit message: disciplines are isolated, and their interplay can somehow influence power structures. As with a lot of digital humanities and cultural studies, there’s also a hint of activism: that building intentional bridges is a beneficial activity, and we’re organizing the community of HASTAC around this goal.
This is what I’ll be commenting on today. First, what does disciplinary isolation mean? I put this historically, and argue that we must frame disciplinary isolation in a rhetorical context.
This brings me to my second point about ontology. It turns out the way we talk about isolation is deeply related to the way we think about knowledge, the way we illustrate it, and ultimately the shape of knowledge itself. That’s ontology.
My third point brings us back to HASTAC: that we represent an intentional community, and this intent is to build bridges which positively affect the academy and the world.
I’ll connect these three strands by arguing that we need a map to build bridges, and we need to carefully think about the ontology of knowledge to draw that map. And once we have a map, we can use it to design a better territory.
In short, this plenary is a call-to-action. It’s my vocal support for an intentionally improved academy, my exploration of its historical and rhetorical underpinnings, and my suggestions for affecting positive change in the future.
Let’s begin at the beginning. With isolation.
Stop me if you’ve heard this one before:
Within this circle is the sum of all human knowledge. It’s nice, it’s enclosed, it’s bounded. It’s a comforting thought, that everything we’ve ever learned or created sits comfortably inside these boundaries.
This blue dot is you, when you’re born. It’s a beautiful baby picture. You’ve got the whole world ahead of you, an entire universe to learn, just waiting. You’re at the center because you have yet to reach your proverbial hand out in any direction and begin to learn.
But time passes and you grow. You go to highschool, you take your liberal arts and sciences, and you slowly expand your circle into the great known. Rounding out your knowledge, as it were.
Then college happens! Oh, those heady days of youth. We all remember it, when the shape of our knowledge started leaning tumorously to one side. The ill-effects of specialization and declaring a major, I suspect.
As you complete a master’s degree, your specialty pulls your knowledge inexorably towards the edge of the circle of the known. You’re not a jack of all trades anymore. You’re an expert.
Then your PhD advisor yells at you to focus and get even smaller. So you complete your qualifying exams and reach the edge of what’s known. What lies beyond the circle? Let’s zoom in and see!
You’ve reached the edge. The end of the line. The sum of all human knowledge stops here. If you want to go further, you’ll need to come up with something new. So you start writing your dissertation.
That’s your PhD. Right there, at the end of the little arrow.
You did it. Congratulations!
You now know more about less than anybody else in the world. You made a dent in the circle, you pushed human knowledge out just a tiny bit further, and all it cost you was your mental health, thirty years of your life, and the promise of a certain future. …Yay?
So here’s the new world that you helped build, the new circle of knowledge. With everyone in this room, I bet we’ve managed to make a lot of dents. Maybe we’ve even managed to increase the circle’s radius a bit!
And, though I’m being snarky about it, it’s a pretty uplifting narrative. It provides that same dual feeling of insignificance and importance that you get when you stare at the Hubble Ultra Deep Field. You know the picture, right?
There are 10,000 galaxies on display here, each with a hundred billion stars. To think that we, humans, from our tiny vantage point on Earth, could see so far and so much because of the clever way we shape glass lenses? That’s really cool.
And saying that every pinprick of light we see is someone else’s PhD? Well, that’s a pretty fantastic metaphor. Makes getting the PhD seem worth it, right?
It kinda reminds me of the cosmological theories of some of our philosophical ancestors.
The cosmos (Greek for “Order”), consisted of concentric, perfectly layered spheres, with us at the very center.
The cosmos was bordered by celestial fire, the light from heaven, and stars were simply pin-pricks in a dark curtain which let the heavenly light shine through.
So, if we beat Matt Might’s PhD metaphor to death, each of our dissertations are poking holes in the cosmic curtain, letting the light of heaven shine through. And that’s a beautiful thought, right? Enough pinpricks, and we’ll all be bathed in light.
But I promised we’d talk about isolation, and even if we have to destroy this metaphor to get there, we’ll get there.
The universe is expanding. That circle of knowledge we’re pushing the boundaries of? It’s getting bigger too. And as it gets larger, things that were once close get further and further apart. You and I and Alpha Centauri were all neighbors for the big bang, but things have changed since then, and the star that was once our neighbor is now 5 light years away.
In short, if we’re to take Matt Might’s PhD model as accurate, then the result of specialization is inexorable isolation. Let’s play this out.
Let’s say two thousand years ago, a white dude from Greece invented science. He wore a beard.
[Note for readers: the following narrative is intentionally awful. Read on and you’ll see why.]
He and his bearded friends created pretty much every discipline we’re familiar with at Western universities: biology, cosmology, linguistics, philosophy, administration, NCAA football, you name it.
Over time, as Ancient Greek beards finished their dissertations, the boundaries of science expanded in every direction. But the sum of human knowledge was still pretty small back then, so one beard could write many dissertations, and didn’t have to specialize in only one direction. Polymaths still roamed the earth.
Fast forward a thousand years or so. Human knowledge had expanded in the interim, and the first European universities branched into faculties: theology, law, medicine, arts.
Another few hundred years, and we’ve reached the first age of information overload. It’s barely possible to be a master of all things, and though we remember scholars and artists known for their amazing breadth, this breadth is becoming increasingly difficult to manage.
We begin to see the first published library catalogs, since the multitude of books required increasingly clever and systematic cataloging schemes. If you were to walk through Oxford in 1620, you’d see a set of newly-constructed doors with signs above them denoting their disciplinary uses: music, metaphysics, history, moral philosophy, and so on.
Time goes on a bit further, the circle of knowledge expands, and specialization eventually leads to fracturing.
We’ve reached the age of these massive hierarchical disciplinary schemes, with learning branching in every direction. Our little circle has become unmanageable.
A few more centuries pass. Some German universities perfect the art of specialization, and they pass it along to everyone else, including the American university system.
Within another 50 years, CP Snow famously invoked the “Two Cultures” of humanities and sciences.
And suddenly here we are
On the edge of our circle, pushing outward, with every new dissertation expanding our radius, and increasing the distance to our neighbors.
Basically, the inevitable growth of knowledge results in an equally inevitable isolation. This is the culmination of super-specialization: a world where the gulf between disciplines is impossible to traverse, filled with language barriers, value differences, and intellectual incommensurabilities. You name it.
By this point, 99% of the room is probably horrified. Maybe it’s by the prospect of an increasingly isolated academy. More likely the horror’s at my racist, sexist, whiggish, Eurocentric account of the history of science, or at my absurdly reductivist and genealogical account of the growth of knowledge.
This was intentional, and I hope you’ll forgive me, because I did it to prove a point: the power of visual rhetoric in shaping our thoughts. We use the word “imagine” to describe every act of internal creation, whether or not it conforms to the root word of “image”. In classical and medieval philosophy, thought itself was a visual process, and complex concepts were often illustrated visually in order to help students understand and remember. Ars memoriae, it was called.
And in ars memoriae, concepts were not only given visual form, they were given order. This order wasn’t merely a clever memorization technique, it was a reflection on underlying truths about the relationship between concepts. In a sense, visual representations helped bridge human thought with divine structure.
This is our entrance into ontology. We’ve essentially been talking about interdisciplinarity for two thousand years, and always alongside a visual rhetoric about the shape, or ontology, of knowledge. Over the next 10 minutes, I’ll trace the interwoven histories of ontology, illustrations, and rhetoric of interdisciplinarity. This will help contextualize our current moment, and the intention behind meeting at a conference like this one. It should, I hope, also inform how we design our community going forward.
Let’s take a look some alternatives to the Matt Might PhD model.
Countless cultural and religious traditions associate knowledge with trees; indeed, in the Bible, the fruit of one tree is knowledge itself.
During the Roman Empire and the Middle Ages, the sturdy metaphor of trees provided a sense of lineage and order to the world that matched perfectly with the neatly structured cosmos of the time. Common figures of speech we use today like “the root of the problem” or “branches of knowledge” betray the strength with which we connected these structures to one another. Visual representations of knowledge, obviously, were also tree-like.
See, it’s impossible to differentiate the visual from the essential here. The visualization wasn’t a metaphor, it was an instantiation of essence. There are three important concepts that link knowledge to trees, which at that time were inseparable.
One: putting knowledge on a tree implied a certain genealogy of ideas. What we discovered and explored first eventually branched into more precise subdisciplines, and the history of those branches are represented on the tree. This is much like any family tree you or I would put together with our parents and grandparents and so forth. The tree literally shows the historical evolution of concepts.
Two: putting knowledge on a tree implied a specific hierarchy that would by the Enlightenment become entwined with how we understood the universe. Philosophy separates into the theoretical and the practical; basic math into geometry and arithmetic. This branching hierarchy gave an importance to the root of the tree, be that root physics or God or philosophy or man, and that importance decreased as you reached the further limbs. It also implied an order of necessity: the branches of math could not exist without the branch of philosophy it stemmed from. This is why today people still think things like physics is the most important discipline.
Three: As these trees were represented, there was no difference between the concept of a branch of knowledge, the branch of knowledge itself, and the object of study of that branch of knowledge. The relationship of physics to chemistry isn’t just genealogical or foundational; it’s actually transcendent. The conceptual separation of genealogy, ontology, and transcendence would not come until much later.
It took some time for the use of the branching tree as a metaphor for knowledge to take hold, competing against other visual and metaphorical representations, but once it did, it ruled victorious for centuries. The trees spread and grew until they collapsed under their own weight by the late nineteenth century, leaving a vacuum to be filled by faceted classification systems and sprawling network visualizations. The loss of a single root as the source of knowledge signaled an epistemic shift in how knowledge is understood, the implications of which are still unfolding in present-day discussions of interdisciplinarity.
By visualizing knowledge itself as a tree, our ancestors reinforced both an epistemology and a phenomenology of knowledge, ensuring that we would think of concepts as part of hierarchies and genealogies for hundreds of years. As we slowly moved away from strictly tree-based representations of knowledge in the last century, we have also moved away from the sense that knowledge forms a strict hierarchy. Instead, we now believe it to be a diffuse system of occasionally interconnected parts.
Of course, the divisions of concepts and bodies of study have no natural kind. There are many axes against which we may compare biology to literature, but even the notion of an axis of comparison implies a commonality against which the two are related which may not actually exist. Still, we’ve found the division of knowledge into subjects, disciplines, and fields a useful practice since before Aristotle. The metaphors we use for these divisions influence our understanding of knowledge itself: structured or diffuse; overlapping or separate; rooted or free; fractals or divisions; these metaphors inform how we think about thinking, and they lend themselves to visual representations which construct and reinforce our notions of the order of knowledge.
Given all this, it should come as no surprise that medieval knowledge was shaped like a tree – God sat at the root, and the great branching of knowledge provided a transcendental order of things. Physics, ethics, and biology branched further and further until tiny subdisciplines sat at every leaf. One important aspect of these illustrations was unity – they were whole and complete, and even more, they were all connected. This mirrors pretty closely that circle from Matt Might.
Speaking of that circle I had up earlier, many of these branching diagrams had a similar feature. Notice the circle encompassing this illustration, especially the one on the left here: it’s a chain. The chain locks the illustration down: it says, there are no more branches to grow.
This and similar illustrations were also notable for their placement. This was an index to a book, an early encyclopedia of sorts – you use the branches to help you navigate through descriptions of the branches of knowledge. How else should you organize a book of knowledge than by its natural structure?
We start seeing some visual, rhetorical, and ontological changes by the time of Francis Bacon, who wrote “the distributions and partitions of knowledge are […] like branches of a tree that meet in a stem, which hath a dimension and quantity of entireness and continuance, before it come to discontinue and break itself into arms and boughs.”
The highly influential book broke the trends in three ways:
it broke the “one root” model of knowledge.
It shifted the system from closed to open, capable of growth and change
it detached natural knowledge from divine wisdom.
Bacon’s uprooting of knowledge, dividing it into history, poesy, and philosophy, each with its own root, was an intentional rhetorical strategy. He used it to argue that natural philosophy should be explored at the expense of poesy and history. Philosophy, what we now call science, was now a different kind of knowledge, worthier than the other two.
And doesn’t that feel a lot like today?
Bacon’s system also existed without an encompassing chain, embodying the idea that learning could be advanced; that the whole of knowledge could not be represented as an already-grown tree. There was no complete order of knowledge, because knowledge changes.
And, by being an imperfect, incomplete entity, without union, knowledge was notably separated from divine wisdom.
Of course, divinity and transcendence wasn’t wholly exorcised from these ontological illustrations: Athanasius Kircher put God on the highest branch, feeding the tree’s growth. (Remember, from my earlier circle metaphor, the importance of the poking holes in the fabric of the cosmos to let the light of heaven shine through?). Descartes as well continued to describe knowledge as a tree, whose roots were reliant on divine existence.
But even without the single trunk, without God, without unity, the metaphors were still ontologically essential, even into the 18th century. This early encyclopedia by Ephraim Chambers uses the tree as an index, and Chambers writes:
“the Origin and Derivation of the several Parts, and the relation in which [the disciplines] stand to their common Stock and to each other; will assist in restoring ‘em to their proper Places”
Their proper places. This order is still truth with a capital T.
It wasn’t until the mid-18th century, with Diderot and d’Alembert’s encyclopedia, that serious thinkers started actively disputing the idea that these trees were somehow indicative of the essence of knowledge. Even they couldn’t escape using trees, however, introducing their enyclopedia by saying “We have chosen a division which has appeared to us most nearly satisfactory for the encyclopedia arrangement of our knowledge and, at the same time, for its genealogical arrangement.”
Even if the tree wasn’t the essence of knowledge, it still represented possible truth about the genealogy of ideas. It took until a half century later, with the Encyclopedia Britannica, for the editors to do away with tree illustrations entirely and write that the world was “perpetually blended in almost every branch of human knowledge”. (Notice they still use the word branch.) By now, a philosophical trend that began with Bacon was taking form through the impossibility of organizing giant libraries and encyclopedia: that there was no unity of knowledge, no implicit order, and no viable hierarchy.
It took another century to find a visual metaphor to replace the branching tree. Herbert Spencer wrote that the branches of knowledge “now and again re-unite […], they severally send off and receive connecting growths; and the intercommunion is ever becoming more frequent, more intricate, more widely ramified.” Classification theorist S.R. Ranganathan compared knowledge to the Banyan tree from his home country of India, which has roots which both grow from the bottom up and the top down.
The 20th century saw a wealth of new shapes of knowledge. Paul Otlet conceived a sort of universal network, connected through individual’s thought processes. H.G. Wells shaped knowledge very similar to Matt Might’s illustrated PhD from earlier: starting with a child’s experience of learning and branching out. These were both interesting developments, as they rhetorically placed the ontology of knowledge in the realm of the psychological or the social: driven by people rather than some underlying objective reality about conceptual relationships.
Around this time there was a flourishing of visual metaphors, to fill the vacuum left by the loss of the sturdy tree.There was, uncoincidentally, a flourishing of uses for these illustrations. Some, like this map, was educational and historical, teaching students how the history of physics split and recombined like water flowing through rivers and tributaries. Others, like the illustration to the right, showed how the conceptual relationships between knowledge domains differed from and overlapped with library classification schemes and literature finding aids.
By the 80s, we start seeing a slew of the illustrations we’re all familiar with: those sexy sexy network spaghetti-and-meatball graphs. We often use them to illustrate citation chains, and the relationship between academic disciplines. These graphs, so popular in the 21st century, go hand-in-hand with the ontological baggage we’re used to: that knowledge is complex, unrooted, interconnected, and co-constructed. This fits well with the current return to a concept we’d mostly left in the 19th century: that knowledge is a single, growing unit, that it’s consilient, that everyone is connected. It’s a return to the Republic of Letters from the C.P. Snow’s split of the Two Cultures.
It also notably departs from genealogical, transcendental, and even conceptual discussions of knowledge. These networks, broadly construed, are social representations, and while those relationships may often align with conceptual ones, concepts are not what drive the connections.
Interestingly, there is precedent in these sorts of illustrations in the history of evolutionary biology. In the late 19th-century, illustrators and scientists began asking what it would look like if you took a slice from the evolutionary tree – or, what does the tree of life look like when you’re looking at it from the top-down?
What you get is a visual structure very similar to the network diagrams we’re now used to. And often, if you probe those making the modern visualizations, they will weave a story about the history of these networks that is reminiscent of branching evolutionary trees.
There’s another set of epistemological baggage that comes along with these spaghetti-and-meatball-graphs. Ben Fry, a well-known researcher in information visualization, wrote:
“There is a tendency when using [networks] to become smitten with one’s own data. Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is ‘complex’ fits just fine with the creator’s own interpretation of it. Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.”
Actually, were any of you here at last night’s Pink Floyd light show in the planetarium? They’re a lot like that. [Yes, readers, HASTAC put on a Pink Floyd light show.]
And this is where we are now.
Which brings us back to the outline, and HASTAC. Cathy Davidson has often described HASTAC as a social network, which is (at least on the web) always an intentionally-designed medium. Its design grants certain affordances to users: is it easier to communicate individually or in groups? What types of communities, events, or content is prioritized? These are design decisions that affect how the HASTAC community functions and interacts.
“We believe that a new configuration in the humanities must be championed to ensure their centrality to all intellectual enterprises in the university and, more generally, to understanding the human condition and thereby improving it; and that those intellectual changes must be supported by new institutional structures and values.”
This was a HASTAC rallying cry: how can the humanities constructively inform the world? Notice especially how they called for “New Institutional Structures.”
Remember earlier, how I talked about the problem if isolation? While my story about it was problematic, it doesn’t make disciplinary superspecialization any less real a problem. For all its talk of interdisciplinarity, academia is averse to synthesis on many fronts, superspecialization being just one of them. A dissertation based on synthesis, for example, is much less likely to get through a committee than a thorough single intellectual contribution to one specific field.
The academy is also weirdly averse to writing for public audiences. Popular books won’t get you tenure. But every discipline is a popular audience to most other disciplines: you wouldn’t talk to a chemist about history the same way you’d talk to a historian. Synthetic and semi-public work is exactly the sort of work that will help with HASTAC’s goal of a truly integrated and informed academy for social good, but the cards are stacked against it. Cathy and David hit the nail on the head when they target institutional structures as a critical point for improvement.
This is where design comes in.
Recall again the theme this year: The Art and Science of Digital Humanities. I propose we take the next few days to think about how we can use art and science to make HASTAC even better at living up its intent. That is, knowing what we do about collaboration, about visual rhetoric, about the academy, how can we design an intentional community to meet its goals? Perusing the program, it looks like most of us will already be discussing exactly this, but it’s useful to put a frame around it.
When we talk about structure and the social web, there’s many great examples we may learn from. One such example is that of Tara McPherson and her colleagues, in designing the web publishing platform Scalar. As opposed to WordPress, its cousin in functionality, Scalar was designed with feminist and humanist principles in mind, allowing for more expressive, non-hierarchical “pathways” through content.
When talking of institutional, social, and web-based structures, we can also take lessons history. In Early Modern Europe, the great network of information exchange known as the Republic of Letters was a shining example of the influence of media structures on innovation. Scholars would often communicate through “hubs”, which were personified in people nicknamed things like “the mailbox of Europe”. And they helped distribute new research incredibly efficiently through their vast web of social ties. These hubs were essential to what’s been called the scientific revolution, and without their structural role, it’s unlikely you’d see references to a scientific revolution in the 17th century Europe.
Similarly, at that time, the Atlantic slave trade was wreaking untold havoc on the world. For all the ills it caused, we at least can take some lessons from it in the intentional design of a scholarly network. There existed a rich exchange of medical knowledge between Africans and indigenous Americans that bypassed Europe entirely, taking an entirely different sort of route through early modern social networks.
If we take the present day, we see certain affordances of social networks similarly used to subvert or reconfigure power structures, as with the many revolutions in North Africa and the Middle East, or the current activist events taking place around police brutality and racism in the US. Similar tactics that piggy-back on network properties are used by governments to spread propaganda, ad agencies to spread viral videos, and so forth.
The question, then, is how we can intentionally design a community, using principles we learn from historical action, as well as modern network science, in order to subvert institutional structures in the manner raised by Cathy and David?
Certainly we also ought to take into account the research going into collaboration, teamwork, and group science. We’ve learned, for example, that teams with diverse backgrounds often come up with more creative solutions to tricky problems. We’ve learned that many small, agile groups often outperform large groups with the same amount of people, and that informal discussion outside the work-space contributes in interesting ways to productivity. Many great lessons can be found in Michael Nielsen’s book, Reinventing Discovery.
We can use these historical and lab-based examples to inform the design of social networks. HASTAC already work towards this goal through its scholars program, but there are more steps that may be taken, such as strategically seeking out scholars from underrepresented parts of the network.
So this covers covers the science, but what about the art?
Well, I spent the entire middle half of this talk discussing how visual rhetoric is linked to ontological metaphors of knowledge. The tree metaphor of knowledge, for example, was so strongly held that it fooled Descartes into breaking his claims of mind-body dualism.
So here is where the artists in the room can also fruitfully contribute to the same goal: by literally designing a better infrastructure. Visually. Illustrations can be remarkably powerful drivers of reconceptualization, and we have the opportunity here to affect changes in the academy more broadly.
One of the great gifts of the social web, at least when it’s designed well, is its ability to let nodes on the farthest limbs of the network to still wield remarkable influence over the whole structure. This is why viral videos, kickstarter projects, and cats playing pianos can become popular without “industry backing”. And the decisions we make in creating illustrations, in fostering online interactions, in designing social interfaces, can profoundly affect the way those interactions reinforce, subvert, or sidestep power structures.
So this is my call to the room: let’s revisit the discussion about designing the community we want to live in.
tl;dr. Don’t rely on data to fix the world’s injustices. An unusually self-reflective and self-indulgent post.
[Edit: this question was prompted by a series of analyses and visualizations I’ve done in collaboration with Nickoal Eichmann, but I purposefully left her out of the majority of this post, as it was one of self-reflection about my own personal choices. A respected colleague pointed out in private that by doing so, I nullified my female collaborator’s contributions to the project, for which I apologize deeply. Nickoal’s input has been integral to all of this, and she and many others, including particularly Jeana Jorgensen and Heather Froehlich (who has written on this very subject), have played vital roles in my own learning about these issues. Recent provocations by Miriam Posner helped solidify a lot of these thoughts and inspired this post. What follows is a self-exploration, recapping what many people have already said, but hopefully still useful to some. Mistakes below shouldn’t reflect poorly on those who influenced or inspired me. The post from this point on is as it originally appeared.]
Someone asked yesterday why I cared enough 1 about gender equality in academia to make this chart (with Nickoal Eichmann).
I didn’t know how to answer the question. Our culture gives some more and better opportunities than others, so in order to make things better for more people, we must reveal and work towards resolving points of inequality. “Why do I care?” Don’t most of us want to make things better, we just go about it in different ways, and have different ideas of what’s “better”?
But the question did make me consider why I’d started with gender equality, when there are clearly so many other equally important social issues to tackle, within and outside academia. The answer was immediately obvious: ease. I’d attempted to explore racial and ethnic diversity as well, but it was simply more fraught, complicated, and less amenable to my methods than gender, so I started with gender and figured I’d work my way into the weeds from there. 2
I’ll cut to the chase. My well-intentioned attempts at battling inequality suffer their own sort of bias: by focusing on measurements of inequality, I bias that which is easily measured. It’s not that gender isn’t complex (see Miriam Posner’s wonderful recent keynote on these and related issues), but at least it’s a little easier to measure than race & ethnicity, when all you have available to you is what you can look up on the internet.
While this problem is far from new, it takes special significance in a data-driven world. That which is countable counts, and damn the rest. At its heart, this problem is one of classification and categorization: those social divides which have the clearest seams are those most easily counted. And in a data-driven world, it’s inequality along these clear divides which get noticed first, even when injustice elsewhere is far greater.
Sex is easy, compared to gender. At most 2% of people are born intersex according to most standards (but not accounting for dysmorphia & similar). And gender is relatively easy compared to race and ethnicity. Nationality is pretty easy because of bureaucratic requirements for passports and citizenship, and country of residence is even easier, unless you live somewhere like Palestine.
But even the Palestine issue isn’t completely problematic, because counting still works fine when one thing exists in multiple categories, or may be categorized differently in different systems. That’s okay.
Where math gets lost is where there are simply no good borders to draw around entities—or worse, there are borders, but those borders themselves are drawn by insensitive outgroups. We see this a lot in the history of colonialism. Have you ever been to the Pitt Rivers Museum in Oxford? It’s a 19th century museum that essentially shows what the 19th century British mind felt about the world: everything that looks like a flute is in the flute cabinet, everything that looks like a gun is in the gun cabinet, and everything that looks like a threatening foreign religious symbol is in the threatening foreign religious symbol cabinet. Counting such a system doesn’t reveal any injustice except that of the counters themselves.
And I’ll be honest here: I want to help make the world a better place, but I’ve got to work to my strengths and know my limits. I’m a numbers guy. I’m at my best when counting stuff, and when there are no sensitive ways to classify, I avoid counting, because I don’t want to be That Colonizing White Dude who tries to fit everything into boxes of his own invention to make himself feel better about what he’s doing for the world. I probably still fall into that trap a lot anyway.
So why did I care enough to count gender at DH conferences? It was (relatively) easy. And it’s needed, as we saw at DH2015 and we’ve seen throughout the digital humanities – we have a gender issue, and a feminism issue, and they both need to be pointed out and addressed. But we also have lots of other issues that I’ll simply never be able to approach, and don’t know how to approach, and am in danger of ignoring entirely if I only rely on quantitative evidence of inequality.
Of course, only relying on non-quantitative evidence has its own pitfalls. People evolved and are socialized to spot patterns, to extrapolate from limited information, even when those extrapolations aren’t particularly meaningful or lead to Jesus in a slice of toast. I’m not advocating we avoid metrics entirely (for one, I’d be out of a job), but echoing Miriam Posner’s recent provocation, we need to engage with techniques, approaches, and perspectives that don’t rely on easy classification schemes. Especially, we need to listen when people notice injustice that isn’t easily classified or counted.
“Uh, yes, Scott, who are you writing this for? We already knew this!” most of you are likely asking if you’ve read this far. I’m writing to myself in early college, an engineering student obsessed with counting, who’s slowly learned the holes in a worldview that only relies on quantitative evidence. The one who spent years quantifying his health issues, only to discover the pursuit of a number eventually took precedence over the pursuit of his own health. 3
Hopefully this post helps balance all the bias implicit in my fighting for a better world from a data-driven perspective, by suggesting “data-driven” is only one of many valuable perspectives.
Upon re-reading the original question, it was actually “Why did you do it? (or why are you interested?)”. Still, this post remains relevant. ↩
I’m light on details here because I don’t want this to be an overlong post, but you can read some more of the details on what Nickoal and I are doing, and the decisions we make, in this blog series. ↩
A blog post on mental & physical health in academia is forthcoming. ↩
WHEREIN I get angry at the internet and yell at it to get off my lawn.
You know what’s cool? Ryan Cordell and friends’ Viral Texts project. It tracks how 19th-century U.S. newspapers used to copy texts from each other, little snippets of news or information, and republish them in their own publications. A single snippet of text could wind its way all across the country, sometimes changing a bit like a game of telephone, rarely-if-ever naming the original author.
Isn’t that a neat little slice of journalistic history? Different copyright laws, different technologies of text, different constraints of the medium, they all led to an interesting moment of textual virality in 19th-century America. If I weren’t a historian who knew better, I’d call it something like “quaint” or “charming”.
You know what isn’t quaint or charming? Living in the so-called “information age“, where everything is intertwingled, with hyperlinks and text costing pretty much zilch, and seeing the same gorram practices.
What proceeds is a rant. They say never to blog in anger. But seriously.
It links to this article on a “map of the world’s scientific research“. Because Vespignani tweeted it, I took it seriously (yes yes I know rt≠endorsement), and read the article. It describes a cartogram map of scientific research publications which shows how the U.S. and Western Europe (and a bit of China) dominates the research world, making the point that such a disparity is “disturbingly unequal”.
“What’s driving the inequality?” they ask. Money & tech play a big role. So does what counts as “high impact” in science. What’s worse, the journalist writes,
In the worst cases, the global south simply provides novel empirical sites and local academics may not become equal partners in these projects about their own contexts.
The author points out an issue with the data: it only covers journals, not monographs, grey literature, edited volumes, etc. This often excludes the humanities and social sciences. The author also raises the issue of journal paywalls and how it decreases access to researchers in countries without large research budges. But we need to do better on “open dissemination”, the article claims.
Hey, that was a good read! I agree with everything the author said. What’s more, it speaks to my research, because I’ve done a fair deal of science mapping myself at the Cyberinfrastructure for Network Science Center under Katy Börner. Great, I think, let’s take a look at the data they’re using, given Quartz’s mission statement about how they always use open data.
I want to see the data because I know a lot of scientific publication indexing sites do a poor job of indexing international publications, and I want to see how it accounts for that bias. I look at the bottom of the page.
This post originally appeared at The Conversation. Follow @US_conversation on Twitter. We welcome your comments at firstname.lastname@example.org.
Alright, no biggie, time to look at the original article on The Conversation, a website whose slogan is “Academic rigor, journalistic flair“. Neat, academic rigor, I like the sound of that.
I scroll to the bottom, looking for the source.
A longer version of this article originally appeared on the London School of Economics’ Impact Blog.
Hey, the LSE Impact blog! They usually publish great stuff surrounding metrics and the like. Cool, I’ll click the link to read the longer version. The author writes something interesting right up front:
What would it take to redraw the knowledge production map to realise a vision of a more equitable and accurate world of knowledge?
A more accurate world of knowledge? Was this map inaccurate in a way the earlier articles didn’t report? I read on.
Well, this version of the article goes on a little to say that people in the global south aren’t always publishing in “international” journals. That’s getting somewhere, maybe the map only shows “international journals”! (Though she never actually makes that claim). Interestingly, the author writes of literature in the global south:
Even when published, this kind of research is often not attributed to its actual authors. It has the added problem of often being embargoed, with researchers even having to sign confidentiality agreements or “official secrets acts” when they are given grants. This is especially bizarre in an era where the mantra of publically funded research being made available to the public has become increasingly accepted.
Amen to that. Authorship information and openness all the way!
So who made this map?
Oh, the original article (though not the one in Quantz or The Conversation) has a link right up front to something called “The World of Science“. The link doesn’t actually take you to the map pictured, it just takes you to a website called worldmapper that’s filled with maps, letting you fend for yourself. That’s okay, my google-fu is strong.
I type “science” in the search bar.
Found it! Map #205, created by no-author-name-listed. The caption reads:
Territory size shows the proportion of all scientific papers published in 2001 written by authors living there.
Also, it only covers “physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering, technology, and earth and space sciences.” I dunno about you, but I can name at least 2.3 other types of science, but that’s cool.
In tiny letters near the bottom of the page, there are a bunch of options, including the ability to see the poster or download the data in Excel.
Ahhhhh I found the source! I mean, it took a while, but here it is. You apparently had to click “Open PDF poster, designed for printing.” It takes you to a 2006 poster, which marks that it was made by the SASI Group from Sheffield and Mark Newman, famous and awesome complex systems scientist from Michigan. An all-around well-respected dude.
To recap, that’s a 7/11/2015 tweet, pointing to a 7/11/2015 article on Quartz, pointing to a 7/8/2015 article on The Conversation, pointing to a 4/29/2013 article on the LSE Impact Blog, pointing to a website made Thor-knows-when, pointing to a poster made in 2006 with data from 2001. And only the poster cites the name of the creative team who originally made the map. Blood and bloody ashes.
Please take a moment out of your valuable time to watch this video clip from the BBC’s television adaptation of Douglas Adam’s Hitchhiker’s Guide to the Galaxy. I’ll wait.
If you’re hard-of-hearing, read some of the transcript instead.
What I’m saying is, the author of this map was “on display at the bottom of a locked filing cabinet stuck in a disused lavatory with a sign on the door saying beware of the leopard.”
The Saga Continues
Okay, at least I now can trust the creation process of the map itself, knowing Mark Newman had a hand in it. What about the data?
Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences
Yep, knew that already, but it’s good to see the sources agreeing with each other.
I look for the data source to no avail, but eventually do see a small subtitle “National Science Foundation, Science and Engineering Indicators.”
Three-quarters-of-the-way-down-the-page, I see it. It’s shimmering in blue and red and gold to my Excel-addled eyes.
Could this be it? Could this be the data source I was searching for, the Science Citation Index and the Social Sciences Citation Index? It sounds right! Remember the technical notes which states “Nearly two thirds of a million papers were published in enumerated science journals in 2001?” That fits with the number in the picture above! Let’s click on the link to the data.
There is no link to the data.
There is no reference to the data.
That’s OKAY. WE’RE ALRIGHT. THERE ARE DATA APPENDICES IT MUST BE THERE. EVEN THOUGH THIS IS A REAL WEBSITE WITH HYPERTEXT LINKS AND THEY DIDN’T LINK TO DATA IT’S PROBABLY IN THE APPENDICES RIGHT?
Do you think the data are in the section labeled “Tables” or “Appendix Tables“? Don’t you love life’s little mysteries?
(Hint: I checked. After looking at 14 potential tables in the “Tables” section, I decided it was in the “Appendix Tables” section.)
Success! The World Bank data is from Appendix Table 5-41, “S&E articles, by region and country/economy: 1988–2003”.
This dataset actually covers both science and social science. It is, you’ll recall, the Science Citation Index and the Social Sciences Citation Index. [edit: at least as far as I can tell. Maybe they used different data, but if they did, it’s World Bank’s fault for not making it clear. This is the best match I could find.]
Which brings us back to Do. The article on Quartz made (among other things) two claims: that the geographic inequality of scientific output is troubling, and that the map really ought to include social scientific output.
And I agree with both of these points! And all the nuanced discussion is respectable and well-needed.
But by looking at the data, I just learned that A) the data the map draws from is not really a great representation of global output, and B) social scientific output is actually included.
I leave you with the first gif I’ve ever posted on my blog:
(p.s. I don’t blame the people involved, doing the linking. It’s just the tumblr-world of 19th century newspapers we live in.)
[edit: I’m noticing some tweets are getting the wrong idea, so let me clarify: this post isn’t a negative reflection on the research therein, which is needed and done by good people. It’s frustration at the fact that we write in an environment that affords full references and rich hyperlinking, and yet we so often revert to context-free tumblr-like reblogging which separates text from context and data. We’re reverting to the affordances of 18th century letters, 19th century newspapers, 20th century academic articles, etc., and it’s frustrating.]
[edit 2: to further clarify, two recent tweets:
FB/Twitter make it easy to reshare with attribution, which is a great step. An even better step would have it suggest a source when you post
Women are (nearly but not quite) as likely as men to be accepted by peer reviewers at DH conferences, but names foreign to the US are less likely than either men or women to be accepted to these conferences. Some topics are more likely to be written on by women (gender, culture, teaching DH, creative arts & art history, GLAM, institutions), and others more likely to be discussed by men (standards, archaeology, stylometry, programming/software).
You may know I’m writing a series on Digital Humanities conferences, of which this is the zillionth post. 1 This post has nothing to do with DH2015, but instead looks at DH2013, DH2014, and DH2015 all at once. I continue my recent trend of looking at diversity in Digital Humanities conferences, drawing especially on these two posts (1, 2) about topic, gender, and acceptance rates.
This post will be longer than usual, since Heather Froehlich rightly pointed out my methods in these posts aren’t as transparent as they ought to be, and I’d like to change that.
@scott_bot@nmhouston don’t get me wrong – i think they’re interesting and useful but i don’t think they’re 100% transparent
As someone who deals with algorithms and large datasets, I desperately seek out those moments when really stupid algorithms wind up aligning with a research goal, rather than getting in the way of it.
In the humanities, stupid algorithms are much more likely to get in the way of my research than help it along, and afford me the ability to make insensitive or reductivist decisions in the name of “scale”. For example, in looking for ethnic diversity of a discipline, I can think of two data-science-y approaches to solving this problem: analyzing last names for country of origin, or analyzing the color of recognized faces in pictures from recent conferences.
Obviously these are awful approaches, for a billion reasons that I need not enumerate, but including the facts that ethnicity and color are often not aligned, and last names (especially in the states) are rarely indicative of anything at all. But they’re easy solutions, so you see people doing them pretty often. I try to avoid that.
Sometimes, though, the stars align and the easy solution is the best one for the question. Let’s say we were looking to understand immediate reactions of racial bias; in that case, analyzing skin tone may get us something useful because we don’t actually care about the race of the person, what we care about is the immediateperceived race by other people, which is much more likely to align with skin tone. Simply: if a person looks black, they’re more likely to be treated as such by the world at large.
This is what I’m banking on for peer review data and bias. For the majority of my data on DH conferences, Nickoal Eichmann and I have been going in and hand-coding every single author with a gender that we glean from their website, pictures, etc. It’s quite slow, far from perfect (see my note), but it’s at least more sensitive than the brute force method, we hope to improve it quite soon with user-submitted genders, and it gets us a rough estimate of gender ratios in DH conferences.
But let’s say we want to discuss bias, rather than diversity. In that case, I actually prefer the brute force method, because instead of giving me a sense of the actual gender of an author, it can give me a sense of what the peer reviewers perceive an author’s gender to be. That is, if a peer reviewer sees the name “Mary” as the primary author of an article, how likely is the reviewer to think the author is written by a woman, and will this skew their review?
That’s my goal today, so instead of hand-coding like usual, I went to Lincoln Mullen’s fabulous package for inferring gender from first names in the programming language R. It does so by looking in the US Census and Social Security Database, looking at the percentage of men and women with a certain first name, and then gives you both the ratio of men-to-women with that name, and the most likely guess of the person’s gender.
Inferring Gender for Peer Review
I don’t have a palantír and my DH data access is not limitless. In fact, everything I have I’ve scraped from public or semi-public spaces, which means I have no knowledge of who reviewed what for ADHO conferences, the scores given to submissions, etc. What I do have the titles and author names for every submission to an ADHO conference since 2013 (explanation), and the final program of those conferences. This means I can see which submissions don’t make it to the presentation stage; that’s not always a reflection of whether an article gets accepted, but it’s probably pretty close.
So here’s what I did: created a list of every first name that appears on every submission, rolled the list it into Lincoln Mullen’s gender inference machine, and then looked at how often authors guessed to be men made it through to the presentation stage, versus how often authors guessed to women made it through. That is to say, if an article is co-authored by one man and three women, and it makes it through, I count it as one acceptance for men and three for women. It’s not the only way to do it, but it’s the way I did it.
I’m arguing this can be used as a proxy for gender bias in reviews and editorial decisions: that if first names that look like women’s names are more often rejected 2 than ones that look like men’s names, there’s likely bias in the review process.
Results: Bias in Peer Review?
Totaling all authors from 2013-2015, the inference machine told me 1,008 names looked like women’s names; 1,707 looked like men’s names; and 515 could not be inferred. “Could not be inferred” is code for “the name is foreign-sounding and there’s not enough data to guess”. Remember as well, this is counting every authorship as a separate event, so if Melissa Terras submits one paper in 2013 and one in 2014, the name “Melissa” appears in my list twice.
So we see that in 2013-2015, 70.3% of woman-authorship-events get accepted, 73.2% of man-authorship-events get accepted, and only 60.6% of uninferrable-authorship-events get accepted. I’ll discuss gender more soon, but this last bit was totally shocking to me. It took me a second to realize what it meant: that if your first name isn’t a standard name on the US Census or Social Security database, you’re much less likely to get accepted to a Digital Humanities conference. Let’s break it out by year.
We see an interesting trend here, some surprising, some not. Least surprising is that the acceptance rates for non-US names is most equal this year, when the conference is being held so close to Asia (which the inference machine seems to have the most trouble with). My guess is that A) more non-US people who submit are actually able to attend, and B) reviewers this year are more likely to be from the same sorts of countries that the program is having difficulties with, so they’re less likely to be biased towards non-US first names. There’s also potentially a language issue here: that non-US submissions are more likely to be rejected because they are either written in another language, or written in a way that native English speakers may find difficult to understand.
But the fact of the matter is, there’s a very clear bias against submissions by people with names non-standard to the US. The bias, oddly, is most pronounced in 2014, when the conference was held in Switzerland. I have no good guesses as to why.
So now that we have the big effect out of the way, let’s get to the small one: gender disparity. Honestly, I had expected it to be worse; it is worse this years than the two previous, but that may just be statistical noise. It’s true that women do fair worse overall by 1-3%, which isn’t huge, but it’s big enough to mention. However.
Topics and Gender
However, it turns out that the entire gender bias effect we see is explained by the topical bias I already covered the other day. (Scroll down for the rest of the post.)
What’s shown here will be fascinating to many of us, and some of it more surprising than others. A full 67% of authors on the 25 DH submissions labeled “gender studies” are labeled as women by Mullen’s algorithm. And remember, many of those may be the same author; for example if “Scott Weingart” is listed as an author on multiple submissions, this chart counts those separately.
Other topics that are heavily skewed towards women: drama, poetry, art history, cultural studies, GLAM, and (importantly), institutional support and DH infrastructure. Remember how I said a large percentage of of those responsible for running DH centers, committees, and organizations are women? This is apparently the topic they’re publishing in.
If we look instead at the bottom of the chart, those topics skewed towards men, we see stylometrics, programming & software, standards, image processing, network analysis, etc. Basically either the CS-heavy topics, or the topics from when we were still “humanities computing”, a more CS-heavy community. These topics, I imagine, inherit their gender ratio problems from the various disciplines we draw them from.
You may notice I left out pedagogical topics from my list above, which are heavily skewed towards women. I’m singling that out specially because, if you recall from my previous post, pedagogical topics are especially unlikely to be accepted to DH conferences. In fact, a lot of the topics women are submitting in aren’t getting accepted to DH conferences, you may recall.
It turns out that the gender bias in acceptance ratios is entirely accounted for by the topical bias. When you break out topics that are not gender-skewed (ontologies, UX design, etc.), the acceptance rates between men and women are the same – the bias disappears. What this means is the small gender bias is coming at the topical level, rather than at the gender level, and since women are writing more about those topics, they inherit the peer review bias.
Does this mean there is no gender bias in DH conferences?
No. Of course not. I already showed yesterday that 46% of attendees to DH2015 are women, whereas only 35% of authors are. What it means is the bias against topics is gendered, but in a peculiar way that actually may be (relatively) easy to solve, and if we do solve it, it’d also likely go a long way in solving that attendee/authorship ratio too.
Get more women peer reviewing for DH conferences.
Although I don’t know who’s doing the peer reviews, I’d guess that the gender ratio of peer reviewers is about the same as the ratio of authors; 34% women, 66% men. If that is true, then it’s unsurprising that the topics women tend to write about are not getting accepted, because by definition these are the topics that men publishing at DH conferences find less interesting or relevant3. If reviewers gravitate towards topics of their own interest, and if their interests are skewed by gender, it’d also likely skew results of peer review. If we are somehow able to improve the reviewer ratio, I suspect the bias in topic acceptance, and by extension gender acceptance, will significantly reduce.
Jacqueline Wernimont points out in a comment below that another way improving the situation is to break the “gender lines” I’ve drawn here, and make sure to attend presentations on topics that are outside your usual scope if (like me) you gravitate more towards one side than another.
Obviously this is all still preliminary, and I plan to show the breakdown of acceptances by topic and gender in a later post so you don’t just have to trust me on it, but at the 2,000-word-mark this is getting long-winded, and I’d like feedback and thoughts before going on.