Our topic is founded on the historical context of American higher education in regards to gender segregation. In colonial America, higher education was gender-segregated, but 19th century all-women’s schools were founded to challenge the notion that women are not capable of pursuing higher education. Nowadays, most US universities are co-educational – there are only 36 single-sex colleges remaining in the country. By examining our own small liberal arts colleges notable histories of gendered education we can compare data from then and now. While Carleton was founded as a co-ed school, Hamilton and Washington & Lee were originally mens colleges, and Vassar, a historically women’s college. Women’s educational rights can also be closely related to the civil rights movements of the 1960s, which largely dealt with the racial desegregation of schools. Although separate issues, the topic of educational equality guides both causes. The history of gender inequality in higher education in America is extensive, and our project will require us to do research on important legislation and policy making at our own school’s approach as well as across the country.

This project explores the histories of access and accessibility at Hamilton College and Vassar College. The guiding principle behind the completion of this project was the visualization of each of our institutions’ involvement and action in their founding mission as it relates to access to marginalized groups. We compared our respective institutions’ modern relationships to access as it relates to 1) accessibility in contemporary usage (disability and accommodation services) and 2) access of each institution’s marginalized group said to be prioritized at the founding. In order to complete this project, newspaper articles; student organization minutes and public correspondence; and pages from official websites were gathered from each institution. Those resources have ultimately been used to visualize and analyze the shifts in meaning and intention around access and accessibility at both colleges, particularly in reference to physical infrastructure and personal accommodations.

By identifying the existence of the dominant perspective and explicitly focusing on stories from outside it, the project is conscious of social bias and proactively against it. the project also is insulated from privilege hazard as it is made up of a diverse group who are speaking to their own experiences. By doing so they quite literally the standard for “empiricism of lived experience”. The project is itself counter data. It is important to clarify that this project isn’t demonstrative of a failing in D’Ignazio and Klein’s critiques as much as it is an example of what digital media ought to be. Rather than ignoring the possibility of cultural bias it is conscious of the social conditions under which it exists and how said conditions affect similar works. The project is an antidote of sorts to the problems Data Feminism lays out.

Expanding the description to encompass information about the vase’s owners, its provenance, and its journey into our museum’s collection would provide a captivating historical backdrop. Additionally, shedding light on the significance of the museum’s acquisition, including the specific funds involved and the role of W. Groke Mickey in procuring this treasure, would offer fascinating insights into its acquisition history. By addressing these gaps and expanding the metadata to encompass personal histories and contextual information, we can unlock a deeper understanding of the characters on the vase. It is through this lens of personal accounts and rich historical context that we can truly appreciate the significance and impact of the vase’s representation of interracial friendship and the struggle against slavery.

n Data Feminism, Catherine D’Ignazio and Lauren Klein explore how power and oppression intersect with digital representation. While they focus mostly on data science, many of their arguments extend beyond the world of big data. Ideas like privilege hazard, the matrix of domination, asymmetrical data extraction, counterdata, data justice (as opposed to data ethics), and more are relevant to just about any work in the digital humanities. Pick a key idea from either chapter 1 or chapter 2 of Data Feminism and apply it to the digital project you’re looking at. Maybe the DH project illustrates the kind of failing that D’Ignazio and Klein critique. Maybe the DH project is an antidote to some of the problems they critique. Maybe it’s both at the same time! In any case, take some time to provide context to the idea from Data Feminism and walk us through your argument.

We will present our results utilizing graphs and paragraphs, including our own analyses of the graphs. Our audience is our classmates and professors. Since all of our sources will come from our college’s archives, we need to double-check with the archivists to make sure there are no intellectual property issues, but since we are using the documents for educational purposes, we do not foresee any problems.

Their History of Co-Education gives a background as to why Vassar chose to go co-ed to begin with, giving insight to what students they were looking to have at Vassar. It details how Vassar, as one of the Seven Sisters, was tightly aligned with the neighboring Ivy League colleges, many of which were still all-men. In 1967, Yale University offered a merger with Vassar, which was the catalyst for Vassar’s decision to not only stay in Poughkeepsie but also grow to become co-educational. Reports from the college showed that students believed staying all-female would result in a “loss of perspective”. An article from Vassar’s Miscellany News dated February 21st, 1968, writes about a similar historically women’s college Sarah Lawrence and their recent admittance of men. Sarah Lawrence and Vassar have a shared history, as former Vassar president Henry McCracken went on to found Sarah Lawrence. There is lots of information within Vassar’s digital archive in regards to the temperament around co-education at the time, and how both students and administration were reacting to it.

Secondly, I looked at “How Your Hometown Affects Your Chances of Marriage.” This New York Times article is based on a Harvard study titled “The Impacts of Neighborhoods on Intergenerational Mobility.” The map included in the article is an example of a choropleth (colored polygon map). It shows people’s marriage patterns across the United States: more specifically, how likely people are to be married at age 26. The best part of this article is that it incorporates factors like class and race, not just geography. For example, the article juxtaposes two maps that show the marriage patterns of poor people and rich people in the South, respectively. The article then explains that “lower-income children in the South are disproportionately black, and marriage rates are also lower among African-Americans.”

In an case, the reality is just that borders don’t always function the same as the simplified maps imply when it comes to indigenous history; there is a lot more movement, overlap, and complexity than one nation on each chunk of land. From a mapping perspective, this is kind of a nightmare. Modern maps didn’t really evolve ways to show this kind of messiness when it comes to nations. There are plenty of very complex military or demographic maps out there, but most people aren’t used to seeing nations overlapping — unless they’re at war or something. The alternative to allowing territories to overlap would have been to put myself in the position of deciding whose territorial claims are valid as opposed to others. I would have had to come up with a strategy to align borders and deal with the reality that some areas were used by more than one nation at different times of the year. Too tough and too much responsibility!

On the brighter side, Voyant offers an easy way to export results. Each tool window conveniently features an export link at the top right-hand corner. This function is invaluable for further analysis or for sharing insights.Beyond providing fascinating data, Voyant sparked new questions and prompted me to rethink my understanding of Les Misérables. For instance, the strong link between ‘man’ and ‘said’ suggested that the narrative is deeply steeped in dialogue or expression of male perspectives. Meanwhile, the co-occurrence of ‘America’ and ‘Freedom’ in most novels instigated me to ponder over possible symbolism or historical references.

This topical issue of the Journal of Digital Humanities is meant to catch and present the most salient elements of the topic modeling conversation: a comprehensive introduction, technical details, applications, and critiques from a humanistic perspective. By doing so, we hope to make topic modeling more accessible for new digital humanities scholars, highlight the need for existing practitioners to continue to develop their theoretical approaches, and further sketch out the relationship between this particular method and those of the broader digital humanities community.

The process of providing context for this pool of data would necessitate questions of stewardship, (Who was responsible for collecting, annotating, and digitizing the data? What were their qualifications? Were they paid a living wage? What communities do they align with? What motivations do they have for collecting the data?) funding, (Why is the institution funding the data collection? What kinds of data are they choosing not to include? Who sponsors their archiving activities?) and content (What art is being included? Who chooses what is included in the archive? What communities are represented by the art that exists in the archive?). Some gaps in the data that might be helpful or interesting to include would be communities or identities that are represented by a particular piece of art. Columns G and H are labeled “culture” and “period,” but there is little data included. Providing keywords that relate to cultural movements, historical art periods, or historical events would be helpful to contextualize the art and realize who is (and isn’t) represented in the collection. This could be done by researching the artist and the period of the art’s creation. Categories of historical periods and cultural/art movements would then have to be created, and the art must then be tagged with their respective labels.

Statistical inference is based on the idea of sampling: that you can infer things about a population (or other large-scale phenomenon) by studying a random and/or representative sample and then mapping those findings back on the population (or phenomenon) as a whole. Say that you want to know who all of the 323 million people in the US will vote for in the coming presidential election. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }

This scenario is not uncommon. Most data arrive on our computational doorstep context-free. And this lack of context becomes even more of a liability when accompanied by the kind of marketing hype we see in GDELT and other Big Dick Data projects. In fact, the 1980s version of these claims is what led Donna Haraway to propose the concept of situated knowledge in the first place.9 Subsequent feminist work has drawn on the concept of situated knowledge to elaborate ideas about ethics and responsibility in relation to knowledge-making.10 Along this line of thinking, it becomes the responsibility of the person evaluating that knowledge, or building upon it, to ensure that its “situatedness” is taken into account. For example, information studies scholar Christine Borgman advocates for understanding data in relation to the “knowledge infrastructure” from which they originate. As Borgman defines it, a knowledge infrastructure is “an ecology of people, practices, technologies, institutions, material objects, and relationships.”11 In short, it is the context that makes the data possible.

This project differs from other forms of media, such as printed books, because it speaks to a wide variety of audiences. Usually, literary works are targeted to college-educated adults, evidenced by complicated academic jargon. Visual storytelling provides an alternative that evens the playing field of accessibility, regardless of classroom experience. Furthermore, visualization tools are opportune for early-childhood education, before children even know how to read. There’s long been a debate about how to approach complicated social issues in the classroom at such a young age. These kid-friendly graphics ensure that the right information is communicated sooner rather than later. This is ever-important in light of the fact that racism is not born, but taught. In Chapter 1 of Data Feminism, D’Ignazio and Klein describe the ‘privilege hazard’ – “the phenomenon that makes those who occupy the most privileged positions among us—those with good educations, respected credentials, and professional accolades—so poorly equipped to recognize instances of oppression in the world.” They explain how, as long as upper-class white men remain in positions of power, data will be warped in favor of dominant groups. This is why it is so important to have diversity on the team. The Slave Voyages project understands the significance of perspective: to amplify marginalized experiences on their own, rather than through the lens of the perpetrator. This ensures that the harsh truth is communicated, rather than one through rose-colored glasses.

“What distinguishes data from other forms of information is that it can be processed by a computer, or by computer-like operations,”

That two objects of activism from Black student unions have archival representation, with the extensively supported object being an older physical artifact and the unsupported object being recent and digital, exemplifies the regard of digital objects as having subordinate historical value. As most modern cultural objects are now “produced and stored in the cloud, often on commercial sites with no guarantee of longevity” (Theimer), greater care in preserving and contextualizing digital objects is critical. Digital preservation is especially prescient given that minoritized identities, often “intimidated at the thought of entering the hallowed halls of the archives [representing] the power of the dominant culture” (Theimer), have migrated to digital spaces as a refuge against marginalizing forces. Instagram was a site of massive popular organization, deradicalization, and solidarity during the Black Lives Matter protests, with minoritized figures instrumentalizing individuals into action through videos and infographics that could disappear without archival attention. We must pay attention to these online sites of culture and legitimize digital cultural objects to archive contemporary history comprehensively.

Challenging power requires mobilizing data science to push back against existing and unequal power structures and to work toward more just and equitable futures..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Seyoon Ahn

Our maternal data is embarrassing,.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }”

Unsworth, three of its key practitioners, entered into discussions with Blackwell Publishing about editing a volume prospectively titled “A Companion to Humanities Computing.” Blackwell wanted a title that might appeal to a wider range of readers and so proposed “A Companion to Digitized Humanities.” Unsworth countered with “Digital Humanities” to keep the field from appearing to be about mere digitization, and the name has stuck, helping to characterize a robust area of research and teaching supported by a number of prestigious conferences, well-received journals, scholarly societies, and even a dedicated office within the National Endowment for the Humanities