Descending from the Ivory Tower – Digital Humanities Beyond Academia

As I already stated in my very first blogpost, I am a first generation Digital Humanist. I started this masters program an academic year ago, in 2015. In a month or so from now, I hope to graduate for the third and final time. I will be thrown under the bus – I mean into the job market – by February. How nice it would be, to stay safely hidden in the Ivory Tower of academia, not having to face whether or not I am truly qualified for the real world. It reminds me of a comment one of my professors once made about the similarity between Italian and Belgian students, remaining under the care of their parents until graduation. I tested myself already, I am perfectly capable of living abroad and taking care of myself, that is, with financial aid and Skype nearby. Now is the time to let it go, to become truly independent. But will I be independent from academia as well?

Stéfan Sinclair puts my internal debate into words in his blogpost on Digital Craft and Humanistic Perspectives Beyond Academia:

Don’t count on an academic job as a reward for your travails (in other words, don’t consider me as a model) and don’t count on your studies to prepare you for easy access to non-academic jobs.
(Sinclair, 2013)

Where do we stand as future masters in Digital Humanities? Do we stick to the tricky search of finding a job as a humanist, albeit some extra capabilities, or do we use our newly found digital confidence to demand a job in the promising world of IT? Is there a middle ground? Is there someting inbetween academia and the outside?

Even for those who do get the change to work on a PhD, possibilities for their academic employment increasingly drop, since the number of tenure track jobs available rapidly decreases for humanities scholars. Another option, discussed by Katarina Rogers, is that of alternative academics, or AltAc:

People with advanced humanities degrees who find stimulating careers in and around the academy but outside the tenure track.
(Rogers, 2015)

Some of those jobs outside the academy can exist in libraries, museums, archives, humanities centres and labs, presses, and so on (Rogers, 2015). In order for students and academics alike to prepare for a job out of the ivory tower, existing programs need to prepare their students adequately for an ever changing job market and society. The Digital Humanities are setting a good example since many of its implicit skills such as “collaboration, project management, and technological fluency” gain importance both within the academy and outside (Rogers, 2015). It is not necessarily about the specific job or career, but

People that identify with the term [alternative academic] tend to see their work through the lens of academic training, and incorporate scholarly methods into the way that work is done.
(Rogers, 2015)

That, together with all the reasons for why the humanities matter, will guide me through the maze. Furthermore, I also believe that the digital in Digital Humanities, increases my opportunities in the current society. Hopefully others will see the importance of the humanities, along with the promising but ever critical digital humanities.


Sinclair, Stéphan. “Digital Craft and Humanistic Perspectives Beyond Academia.” 2013.

Rogers, Katina. “Humanities Unbound: Supporting Careers and Scholarship Beyond the Tenure Track.” Digital Humanities Quarterly, 9(1), 2015.

Social (Media) Data: a gold mine for Digital Humanities?

Lev Manovich identifies two types of data used in social and cultural studies during the twentieth century: “‘surface data’ about lots of people and ‘deep data’ about a few individuals or small groups” (Manovich, 2012). An intermediate method is used in statistics, where a researcher chooses a sample to represent an entire country for example. Comparing this aproach to Photoshop, Manovich goes on to say:

A “pixel” that originally represented one person comes to represent one thousand people who are all assumed to behave in exactly the same way. (Manovich, 2012)

This is exactly what happened in polls preceding the recent Presidential Elections in the United States of America, and predicting that Hillary Clinton would win. Nate Silver’s FiveThirtyEight 2016 general election forecast predicted Donald Trump would only have a 28.2% chance of winning, although they estimated a 10% chance that Clinton would win the popular vote and lose the electoral college vote. Their model containing three versions needs quite a lengthy user guide, their polls-plus version of the model combining polls with an economic index and each version following four major steps:

  1. Collect, weight and average polls. – based on Pollster ratings.
  2. Adjust polls.
  3. Combine polls with demographic and (in the case of the polls-plus) economic data.
  4. Account for uncertainty and simulate the election thousands of times.

Nate Silver later defended his model in Why FiveThirtyEight Gave Trump A Better Chance Than Almost Anyone Else saying that:

People mistake having a large volume of polling data for eliminating uncertainty. […] the polls sometimes suffer from systematic error: Almost all of them are off in the same direction. (Silver, 2016)

The four objections Manovich states with regard to the rise of social media and new computational tools that can process massive amounts of data (Manovich, 2012) can help explain why The Polls Missed Trump.

The first objection Manovich describes, is the lack of availablity of data outside of the social media companies, specifically for transactional data (Manovich, 2012). In the case of the election polls, one of the recurrent errors is the nonresponse bias, or “failing to get supporters of one candidate to respond with the same enthusiasm as supporters of his opponent” as Carl Balik and Harry Enten stated in their article asking Pollsters Why.

The second objection Manovich formulates, is the lack of authenticity, since communications over social media and digital footprints are often carefully curated and systematically managed (Manovich, 2012). However, “several pollsters rejected the idea that Trump voters were too shy to tells [sic] pollsters whom they were supporting” (Balik and Enten, 2016). However, automated-dialer calls which used a recorded voice registered more Trump voters as opposed to live-interviews (Balik and Enten, 2016).

Manovich also raises a third objection, referring to the size versus depth issue since different data leads to different questions, patterns, and insights (Manovich, 2012). In the aftermath of the elections, many explinations for why the polls were off took the stage in several articles. Even on the FiveThirtyEight website, I found at least three articles offering a different point of view, even contradicting each other: Jed Kolko explaining Trump Was Stronger Where The Economy Is Weaker, Carl Bialik stating that Voter Turnout Fell, Especially In States That Clinton Won, but also claiming No, Voter Turnout Wasn’t Way Down From 2012, whereas Clare Malone blamed the outcome on the sentiment of Americans Don’t Trust Their Institutions Anymore. The differing approaches even amonst the same redaction team shows how some refer to individuals’ emotions, while others state voter turnout or a weak economy.

Finally Manovich’s fourth objection points out the need for specialized expertise especially in computer science, statistics and data mining, needed to work on large data sets and especially combining the data as Nate Silver did for his general election forecast. Even though he clearly has a well-defined method, adding several factors and ranking pollsters based on historical data on their accuracy, polling needs to “get more comfortable with uncertainty” (Balik and Enten, 2016). One of the people they interviewed even went as far as to state that “the incentives now favor offering a single number that looks similar to otheer polls instead of really trying to report on the many possible campaign elements that could affect the outcome. Certainty is rewarded, it seems” (Balik and Enten, 2016).

If Digital Humanists want to make sure that:

The rise of social media, along with new computational tools that can process massive amounts of data, makes possible a fundamentally new approach to the study of human beings and society. (Manovich, 2012)

We need to change how students in humanities are being educated, something the Advanced Master in Digital Humanities of the KU Leuven is certainly trying to achieve.


Manovich, Lev. “Trending: The Promises and the Challenges of Big Social Data.” Debates in the Digital Humanities. Minneapolis, MN: University of Minnesota Press, 2012.

Silver, Nate. “2016 Election Forecast.” FiveThirtyEight, November 8, 2016.

Balik, Carl, and Enten, Harry. “The Polls Missed Trump. We Asked Pollsters Why.” FiveThirtyEight, November 9, 2016.

Genderizing Digital Humanities

During our class on Digital Humanities, Politics and Gender we discussed diversity related issues. I would like to start by quoting the definition of diversity:

The condition or quality of being diverse, different, or varied; difference, unlikeness.
(Oxford English Dictionary)

However, diversity can be better understood in terms of intersectionality, which has moved beyond the race-class-gender relationship as described by legal scholar Kimberlé Crenshaw (Crenshaw, 1991), now also including what Roopika Risam specifies as “additional axes of difference including sexuality and ability”. Risam also adds that “as a lens for scholarship [in the digital humanities], intersectionality resists binary logic, encourages complex analysis, and foregrounds difference” (Risam, 2015).

Since I am mostly interested in the gender equity problem, without ignoring the other aspects of intersectionality, I would like to go into detail on society’s ever persistent binary logic when it comes to gender. First, let’s take a look at the psychological and sociological use of the term gender I am referring to, which originated in the United States.

The state of being male or female as expressed by social or cultural distinctions and differences, rather than biological ones; the collective attributes of traits associated with a particular sex; or determined as a result of one’s sex. Also: a (male or female) group characterized in this way.
(Oxford English Dictionary)

Even within this very considerate definition, the binary persists. According to the Oxford English Dictionary it is either male or female, not even including “other” and still very much linked to an individuals biological sex, which (just so you know) can also differ from male or female. As a psychologist, Rose Marie Hoffman moves away from this definition of gender as collective attributes, instead discussing Gender Self-Definition and Gender Self-Acceptance (Hoffman, 2006).

Hoffman describes the stages of the Feminist Identity Model (Downing and Roush, 1985) based on Cross’s Black Identity Development Model (Cross Jr, 1971) as:

(a) unawareness of inequity and discrimination through
(b) experience of crises that force one to confront such inequities to
(c) an immersion and identification with one’s own group that provide opportunities for
reflection and exploration to
(d) integration of one’s experiences around the area of
oppression and a concomitant achievement of balance (i.e., able to evaluate people
as individuals instead of only as group members) and, finally, to
(e) a commitment to meaningful action toward eliminating the ism involved.
(Hoffman, 2006).

This Feminist Identity Development also shows some similarities with the sex role transcendence theory by Rebecca et al., which falls into three phases: a lack of awareness of gender roles, a polarization stage, and the transcendence of gender role stereotypes (Rebecca et al., 1976). The womanist position differs from the feminist position in that it
recognizes “poverty, racism, and ethnocentrism as equal concerns with sexism” (Henley
et al., 1998).

Where does Digital Humanities come in, you might ask yourself. I would like to refer to Nickoal Eichmann, Jeana Jorgensen, and Scott B. Weingarts’ study of fifteen years of DH conferences (2000-2015). They did so

(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.
(Eichmann et al., 2016)

However, in order to study the lack of diversity, in the name of data quality, they acknowledge “the gross and problematic simplifications involved in this process of gendering authors without their consent or input” (Eichmann et al., 2016). In their own defense they state that “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 women” (Eichmann et al., 2016). I can, to a certain extent, agree that gender bias stems from the perception of a person’s gender, rather than the gender they identify with. The question remains however, whether you can assume others to identify authors as male, female, or unknown/other.

There are several tools to test your own bias, such as, and Fortunately, and probably after working on my master thesis considering the gender balance in both Computer Science and Digital Humanities for a year, my result suggests “little to no association between female and male and science and liberal arts”. This test again demonstrates a binary definition of gender, confirming my main concern.


Kimberle Crenshaw. Mapping the margins: Intersectionality, identity politics, and violence
against women of color. Stanford Law Review, 43(6):1241–1299, 1991. ISSN
00389765. URL

Roopika Risam. Beyond the margins: Intersectionality and the digital humanities. DHQ:
Digital Humanities Quarterly, Volume 9, Number 2, 2015.

Rose M. Hoffman. Gender self-definition and gender self-acceptance in women: Intersections
with feminist, womanist, and ethnic identities. Journal of Counseling and
Development : JCD, 84(3):358–372, Summer 2006. URL http://search.proquest. com/docview/219028098?accountid=17215.

Nancy E. Downing and Kristin L. Roush. From passive acceptance to active commitment:
A model of feminist identity development for women. The Counseling
Psychologist, 13(4):695–709, 1985. doi: 10.1177/0011000085134013. URL http: //

William E Cross Jr. The negro-to-black conversion experience. Black world, 20(9):
13–27, 1971.

Meda Rebecca, Robert Hefner, and Barbara Oleshansky. A model of sex-role transcendence.
Journal of Social Issues, 32(3):197–206, 1976.

Nancy M Henley, Karen Meng, Delores O’Brien, William J McCarthy, and Robert J
Sockloskie. Developing a scale to measure the diversity of feminist attitudes. Psychology
of Women Quarterly, 22(3):317–348, 1998.

Nickoal Eichmann-Kalwara, Jeana Jorgensen, and Scott Weingart. Representation at
Digital Humanities Conferences (2000-2015). prepublished, 3 2016. URL 10.6084/ m9.figshare.3120610.v1.