The Final Project offered an opportunity to apply theories and explored subjects discussed this semester. As I worked, I broke it into two parts: there was the DH project and the project proposal.
DH Project: The eBay Fashion Exhibit
After the data visualization praxes, I knew I wanted to explore vintage or secondhand ecommerce platforms in more depth. I thought I was interested in internet research and digital cultures, and some of the casual observations I made about how the listings were written and how that data could be presented visually solidified that notion. I bookmarked them as ideas to revisit.
Finding related projects helped refine my idea to reimagine content from eBay listings. Tega Brain’s Post The Met and the #exstrange project served as examples of how to decontextualize these digital artifacts to ask questions about digital platforms and cultural authority. From there, it became much easier to identify a theoretical framework. David Nieborg and Thomas Poell’s article, The platformization of cultural production: Theorizing the contingent cultural commodity articulated how to analyze the phenomenon of platformization in cultural industries (2018). Using their examination of news and gaming industries as case studies, I could better articulate how market forces, governance, and infrastructures at play impacted the vintage fashion item as a cultural commodity.
Project Proposal
Knowing grant writing takes a very specific form, I was nervous about how to approach the proposal. I had never written one before, and at first I found it difficult to know how the work I did to formulate the project and ground it theoretically contributed to the proposal. I sought examples of successfully funded grant applications to better understand what to include and how to do so. Eventually, I understand how to articulate the project’s purpose and activities.
Ultimately, I enjoyed the process of creating and proposing a DH project, even though at the beginning, I just wanted to build.
I compared reviews of Dries Van Noten runway shows from 2000-2014 using the stylecom_reviews dataset from the Harvard dataverse. (In 2000, Style.com was founded by Condé Nast and launched as the online supplement for their print publications Vogue and W and hosted original content.) I limited the documents in the corpus to Belgian brand Dries Van Noten with the hopes of seeing how the collections, and critics’ impressions of them, changed over time. Since the founder and brand’s namesake, Dries Van Noten, remains the only Designer/Chief Creative Officer in the label’s history, the interwoven brand and designer ethos allows one to consider this more of a single body of work, a continuous expression through fashion design, than a corpus composed of reviews of multiple brands or multiple designers.
Before diving into the experience, I want to expand on the nature of the documents in the corpus, and what a distant reading of them may reveal. Given that these are reviews of collections, it’s important to clarify the role of criticism in fashion. In May 2023, Robin Givhan, Senior Critic-at-Large for the Washington Post and Pulitzer Prize winner for criticism, said to Interview Magazine, “The role of the critic, whether it’s a critic who’s writing about fashion or visual arts or music, is the same. It’s to try and help the reader forge connections, to look at things in a different way. It’s to help them to navigate a fire hose of information and ideas, to provide a framework for thinking about the subject matter.” Let’s consider the reviews as the critics’ efforts to contextualize the collection and show experience for the reader, and not a direct expression from the designer.
I saved each review as a text file and uploaded them all to Voyant. What follows attempts to describe Dries Van Noten’s design leanings through criticism at a high level while also capturing how his expression morphs over time.
Most Frequent Words:
The first few most frequent words may not be a surprise, as one might expect reviews to reference the brand and designer names, a collection, and show by nature of the subject. It could be interesting to take up a comparative analysis between designers to see if critics refer to designers or brands as frequently, and how they do so, as that may indicate the perceived strength of a designer’s identity in their work.
A sense of clothing and design elements emerge, as well. Distant readers may understand Van Noten as a designer who embraces a number of visual styles (e.g., prints, gold, white, color, black) and techniques (e.g., embroidered) across a variety of garments (e.g. jackets, pants, coats, skirt/skirts, dresses) with common design or styling elements (e.g., long, high, shapes).
Distinctive Words:
While the most frequent words in the corpus propose a general design identity, the distinctive words reveal unique design choices, inspirations, and impressions across individual collections and shows.
Contexts:
“Like” is among the most frequent words in the corpus, and I wanted to understand if it was used in any comparisons or just to list examples. Taking a closer reading of the text reveals a number of compelling comparisons that contextualize design and show elements to the reader.
Simile:
the front of the body like a religious garment (Fall 2002)
had a well-loved feel, like a beautiful gold couture piece (Fall 2003)
characters on this runway looked like refugees from the Mitteleuropean 1940s (Fall 2005)
particularly stunning were the prints, which looked like old tapestries (Spring 2005)
It’s that kind of research and informed reflection that makes a Dries show like a visit to a glamorous library (Spring 2012)
[To compare the marriage of luxurious, soft, beautiful fabrics with hardy, rough, structured ones in the collection.] It was just like the royal family (French, Russian, pick another) disguising themselves as peasants in a futile attempt to escape the revolution (Spring 2014)
Daiane Conterato’s resolute little face looking like she was ready to flamenco (Spring 2014)
The exercise offered an interesting interrogation of the visual, tactile, and audible experience of seeing clothing and attending a fashion show given that I didn’t look at images from the shows, and readers may have only be presented a handful when these reviews were first published. I thought about A Massively Addressable Object by Michael Witmore in this week’s readings where he asserts that “a text is a text because it is massively addressable at different levels of scale.” For this analysis, there is validity in comparing this corpus with another designer’s as a single word (e.g., gold, prints, shapes, embroidered, organza, mousseline) can convey great detail about the subject.
In The Questions of Minimal Computing, Roopika Rissam and Alex Gli define Minimal Computing, a task they admit is likely idealistic and difficult, as “a mode of thinking in Digital Humanities praxis that resists the idea that innovation is defined by newness, scale, or scope.”
Throughout much of the piece, Rissam and Gil present minimal computing to be a stance taken as the result of resource scarcity. Their approach to make choices on what’s most important given what resources are available aims to develop a DH that is widely accessible and more equitable to people who have been excluded from, or had no control over, the production of their communities own knowledge in other projects. As discussed in previous readings and classes, certain elements of DH projects, from collecting and constructing archives, to the choice in scientific graphics or computational methods may reinforce colonialist and unethical biases. One great result of this conversation around minimal computing is that it forces us to consider the systems in which projects are developed and executed when encountering them and evaluating the final projects.
There are potential downsides to always eschewing technically advanced projects or solutions. One I can think of is the loss of nuance when using simpler graphics or static websites that may produce a more abstracted final project than a map that can zoom or visual representations of intersectionality. Much of DH work is about slowing down and making a mess of a subject to represents a its complexities. While simpler solutions may inspire researchers to find creative solutions, how to balance detail and abstraction must be considered when planning a project. While trying to balance resources and their visions, DH practitioners shouldn’t hesitate to learn and use more technically innovative or advanced methodologies, especially given the value that humanists can bring to conversations around fast-developing technologies like Machine Learning and AI.
On September 26, I attended the Quantitative Methods & Reasoning Across Disciplines workshop hosted by the Quantitative Research Consulting Center and the Interactive Technology and Certificate Program as part of their Fall Skills Lab series. The two hour session covered topics in statistical analysis, data collection, and research design to empower an the group to incorporate quantitative approaches in the own projects.
The workshop served several purposes, the first being a refresher in fundamental and more intermediate topics in Statistics, while the rest of the time was spent with an eye toward applying those methods in research. In the first, and simplest portion on Statistics, we discussed measures of centrality in a dataset (e.g., Mean, Median, and Mode), as well as ranges within a dataset the as it relates to skew and volatility. I was familiar with these concepts already from undergraduate Statistics.
From there, we discussed measures of correlation and when it was appropriate to use each one. The Pearson Correlation Coefficient and Linear Regression both describe the relationships between two variables. I remembered both from Algebra and Statistics at one point or another, but was happy to think about them in the context of my interests within the Digital Humanities. The Pearson Correlation Coefficient is a value ranging from 0 to 1 or -1, indicating the strength of the relationship in either a positive (i.e., change in one variable leads to a change in the same direction of the other) or negative (i.e., change in one variable leads to a change in the opposite direction of the other) direction. Linear Regression also describes how one variable impacts another with the formula y = mx + b, but should be used when trying to predict another value based on one you already have. I was much less comfortable with the forms of Supervised and Unsupervised Classification we spoke about next.
Logistic regression is a supervised classification model used when your dataset contains two possible values (e.g., whether or not someone is employed job). The model is supervised because it is trained on a dataset. K means clustering is an unsupervised classification scheme that breaks data down into identifiable groups by partitioning the set down into clusters organized around the nearest mean. It is considered unsupervised because the algorithm learns patterns without being trained.
We concluded the workshop by discussing research design, touching on randomized experiments, natural experiments, and survey analysis. The last two, natural experiments, which are more so observational studies, and surveys seemed more apt for Digital Humanities subjects, it was helpful to understand how research is carried out to better understand academic works we read, as well.
I found the session to be a great resource and a really strong grounding in quantitative social science methods that I will keep in mined while pursuing my own projects and research within DH.
I used Tableau to create a visualization comparing Craigslist furniture listings in the three largest cities in the United States: New York, Los Angeles, and Chicago.
Craigslist is fairly easy to scrape because the format is consistent across pages. I used the BeautifulSoup and pandas packages in Python to pull the listing title, price, and listing location (i.e., neighborhood, town, borough, zip code) of furniture listed in for sale in each city into a dataframe, which I exported as a csv file. I did not “clean” the listing titles or locations, and I’m glad I didn’t, because retaining those two fields in their “raw” state raised some of the questions with data visualization that Johanna Drucker and Lev Manovich discussed in their respective articles.
Once I had the data, I went to Tableau. The public platform is robust and allows users to upload tabular or text data for analysis and visualization, and to combine those visualizations into a dashboard. Users can upload more than one file and combine individual tables as you would with relational databases, but you don’t need to know SQL in order to do so.
Initially, I found myself struggling to actualize graphical representations I had in my head as a result of the crowded interface and levels of abstraction. I am used to more explicitly transforming data with functions or formulas through code, with a more minimal visualization functionality in Periscope/Sisense, which is a different business intelligence platform that connects directly into your database and generates charts off code. It took time to figure out where I needed to drag-and-drop a field in order to add it to the x vs the y axis. For example, the first time I dragged the City field into the workspace, it generated coordinates and a map popped up. Its ability to infer what format to present your data in is both helpful and frustrating.
The platform also felt grounded in traditional info visualization the favored bar charts, pie charts, and line graphs, regardless of the data uploaded, which makes sense given that it is largely an enterprise software. I’d also wager this is why Tableau offers little documentation, favoring few example videos and recommending user join a community of others to learn.
With that being said, I was able to explore the dataset and create a final dashboard that made use of statistical charts, while also finding the edges of it’s utility in my current exploration, with my current knowledge of the tool.
In trying to reconcile Neighborhoods, Boroughs, and towns outside from each city, I was reminded of what Johanna Drucker said in “Humanities Approaches to Graphical Display”: that no data pre-exists its parameterization. They are already interpreted values. Any patterns observed or conclusions drawn from location with such fluid boundaries would be subject to criticism given the contested history of neighborhoods in major US cities. In addition, this data was user generated, which might make it easier to understand that the labels and classifications are subjective and influenced by the person creating the listing. I wondered about ways I could use Tableau in ways that were more temporal or flexible, as Drucker described.
I also found myself wanting to explore direct visualization, since I took interest in the listing metadata. Listing titles were long strings of keywords, and the locations were far from standard. I attempted to combine similar or duplicate locations that followed a slightly different format, but debated whether or not that was my role, for reasons mentioned above. The uncleaned listing titles, locations, and images feel critical to understanding cultural, social, and economic ideas underpinning craigslist, yet it was difficult to find ways to incorporate the media objects themselves, other than adding in screenshots and returning the fields themselves.
Ultimately, the platform does enable laymen to create well-crafted visualizations, and I am please with the initial visualizations I put together.
With this week’s readings on Mapping, I reflected on the digital maps I use everyday. In one trip to meet up with friends at a new restaurant, I may open Apple Maps on my phone to see what street the restaurant is on and which route I need to take, what the weather looks like for the next few hours, and whether or not public transportation is on time or delayed. The maps on my phone allow me to consider all of this in the five minutes before I head out the door.
In “How to Lie with Maps,” Mark Monmonier describes the three attributes (i.e., scale, projection, and symbolization) maps are comprised of. In considering my regular use of these tools, symbols proved the most relevant, at least on the surface. All of them use shape, orientation, and hue in a number of ways.
Apple Maps: There are symbols to classify different vehicles or modes of transportation, places of interest, routes, color/hue for traffic density, to name a few. Apple maps provides a ratio scale that adjusts as you zoom or rotate the map, although as a non-driver in New York City, I less frequently think in distance, rather defaulting to “stops” as my unit of measurements, even though those are not standard.
Accuweather: Weather apps use a variety of symbolic elements to convey information. One of the most noticeable for this subject is color, as it relates to precipitation. The app I use provides four different color spectrums to indicate the severity of four different types of precipitation: rain, snow, ice, and mix. To Apple’s credit, if your directions include public transportation, it tells you how many stops you need to travel on a particular line.
NYC Subway: The colored subway lines can act as indicators and abstractions, a quick point of reference if, like me, you forget which lettered trains provide express service, but know you take a yellow line to and from The Graduate Center. On top of that, colored service alerts indicate good service or delays.
What I’m not considering in the five minutes before I leave my apartment, are the ways the information I’m presented is influenced by outside forces, accessibility of the symbols I’m interpreting, and the convenience afforded to me by living in the West.
First, businesses and other entities don’t automatically appear on these platforms. The three most used navigation applications, Google Maps, Waze, and Apple Maps, which combined occupy 95% of audience reach, have mechanisms in place for businesses to make themselves visible, all by way of marketing or advertisement. Google and Apple require business owners to create profiles via their respective services, and then use a variety of criteria to rank which businesses are displayed to users. Waze requires customers to pay to place adds that pop up as destinations on a map a users drive by. In these cases, English proficiency, access to internet enabled technology, and cost, among other things, act as barriers and restrict what is and isn’t a “place of interest.”
Red and green feature prominently as symbols, often working at ends of a spectrum, indicating the severity of precipitation, the quality of train service, and the status of traffic congestion on the roads. It is also the most common type of color vision discrepancy.
In “Dividing Lines,” Mayukh Sen highlights the disparity in utility of these tools and their failure to capture the landscape of former colonies, often in the Global East or Global South, in a way that effectively renders them unimportant. In a time where everything feels like it’s at our fingertips, enabled by the internet, exclusion is dangerous because the phenomenon persists despite the common notion that the internet has charted everything. This reinforces the impacts and ideals of colonialism by rendering what is covered, often in the West or in English speaking areas, visible and the rest untraceable.
As I traveled around town this weekend, I tried to consider where I encountered maps, how I used them, and what their limits were. I would love to hear, or discuss in class, others’ observations.
The peer-reviewed journal Reviews in Digital Humanities centers the belief held by Stephan Ramsay that digital humanists create things. The project deploys principles of open access and transparency to facilitate the production and maintenance of digital scholarship. The site defines this output as “digital archives, multimedia or multimodal scholarship, digital exhibits, visualizations, digital games, digital tools, and digital projects.” By centering projects over traditional articles, it also challenges what academic output could look like inline with the tendency among many in the field to be disruptive, as well as generative.
The site provides DH practitioners with a free platform to submit, review, and publish digital projects, stating that their goal is “to foster critical discourse about digital scholarship in a format useful to other scholars.” By providing digital humanists with infrastructure to easily review work and have their work reviewed, it makes the process of building projects approachable and attainable. This accessibility may also make it easier for communities traditional marginalized from academia to join in.
Reviews in Digital Humanities also archives digital projects in a variety of ways that make it easy for users to browse and engage with. The Issues tab catalogues different volumes. The projects are also searchable by Alphabet, Time Period, Field or Study, and Topic under the Project Registry. This creates a robust experience, allowing for casual exploration of different topics and ontologies, as well as more direct searches.
Themes of open access, collaboration, and justice appeared throughout our readings this week. Reviews in Digital Humanities leverages these ideas to help scholars build projects and knowledge across a variety of subjects, making the case that the projects can define DH.
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