If there is one thing that I gathered from taking this digital humanities course, it is that technology and data, does not seek to benefit everyone. From machine learning and prediction models with racist biases ingrained into them to digital maps being effectively useless to lands that aren’t inhabited by the western white man, everyday use technology can be a prime example of the legacies of colonialism. The article “Dividing Lines” by Mayukh Sen looks into the Google Earth platform and the difference in how it is used in real life between the western world and the global south, as well as how the portrayal of the platform are inaccurate.

The story of Saroo Brierly getting split from his family on a train and ending up 900 miles away forcing him into an orphanage only to find his family many years later, is no doubt an amazing story. I could only imagine the feeling of knowing your family is somewhere out in the world but, having no contact with them or truly knowing where they are. His use of Google Maps and vague memories together ended up reuniting him and his family in the end. The author of the article said the movie depicted the reunion as a win for Brierley and more of a win for Google Maps. As if the entire movie was one big advertisement for platform. The movie left out key realities about using Google Maps in the Global South. Those realities are that Google Maps does not care about these places. From the authors experience and my own, finding our hometowns in the United States come with no visual hiccups. No outdated or grainy maps and basically accurate depictions of what we would see there on a daily basis. Although when the author looks for his mother’s hometown in India, that he can’t even write in English. There are different results and to make things worse, zooming in on some of the results are basically visually useless. These problems did not arise in the movie Lion instead, the only problem with Brierley had to overcome was his lack of memory from his past. I find this the most interesting part about the article because we see this in different context all the time. As westerners, we love to romanticize adversity and struggle and turn it into a story about overcoming it. Oftentimes, there is a white man savior complex being buried into the message and here, we see that complex on display in the form of Google Maps. Basically, we have the immigrant foreign brown kid being saved by an all-knowing western technology that is portrayed to have no issues and not a hint of the legacies of colonialism. Yet clearly, if you open the platform on your own and look around at some of these global south communities, your results may vary. For starters, in my opinion, I think we need to point this out more in Hollywood because the white savior complex runs rapid there. Also, we need to question companies such as Google about some of their decisions they make with their platforms. Why do you neglect the global south? Why do you use these places as testing grounds? When satellite images are clearly available,  why are some parts of your maps so outdated and in such poor quality? The legacies of colonialism will continue to prevail unless we demand those who benefit the most from colonization to level the playing field.

During this semester, we’ve understood data to mean numerical facts and statistics. Scholars can represent those descriptive numbers and aggregations along two dimensional coordinate systems and maps, communicating an understanding of the texts or medium under study by abstracted details into summaries. Contributions like Manovich’s Exploring One Million Manga Pages with Supercomputers and HIPerSpace “ use visualization and/or mathematical models to describe the space of possible and realized variations” by clustering the amount of gray scale or measuring the level of detail in illustration among 1,074,790 pages. Others employ methods like distant reading to process words and sentences in large corpora as way of confirm the coherency of writerly social characteristics, and ponder the examples which evade their models (see So and Roland’s stellar Race and Distant Reading). These flavors of analysis view data as a means push away from subjective knowledge of artistic output, proposing dimensions and measures as appropriate tools for grappling with humanities as scholarly sport. The quantization champions of digital humanities offer mostly rigorous and often legitimate intellectual insight to their subjects of study. Still, there is a more capacious views on data, a more digitally native observation, beyond the statistical definitions we’ve been exposed to this semester.

CUNY’s own Kevin L. Ferguson stands as starting point for many members of this minority group of data “analysts.” Looking at his frequently updated tumblelog Film Visualization, it’s easy to mistake his methods as art (in fact, he courted some controversy when an artist appeared to ape his methods). As a part of his scholarly work, Ferguson produces summed frame visualizations. Screenshots of a movie are superimposed on themselves by loading them into the open source medical imaging software ImageJ, provided by the NIH.

While comparisons on the corpus and categorical level are made by “automatic process which reveals otherwise unconscious information about film texts” (DHQ: Digital Humanities Quarterly: Digital Surrealism: Visualizing Walt Disney Animation Studios) what differentiates these summed frame ouputs from more common visualization methods is two fold. Firstly, this method takes digital data, in the form of images, as material from which to create a subjective understanding of the objects under study. While there are numerical transformations involved in the production of these images, the artifacts generated require context and knowledge of the specific films under analysis, and are not summarized by statistics. Contrast, color and framing of subjects can be determined for each movie, or for the movies of a corpus, but the visualizations themselves are only a component in the over all conclusions one can draw. Secondly, these summed frames suggest a mode of experiencing or “reading” that is more embodied than typical distance reading. The scholar may see a perpondance of close-ups in a given corpus rather than talling the number of such shots, or may observe the lack of vue variation in a color film.

Another digital humanist with an expansive view of data and its expression is Kim Brillante Knight. Her project “Danger, Jane Roe!”, described here, cleverly grounds itself in a tradition of feminist praxis. Knight fashioned embrodery of reproductive anatomy undergirded by LilyPads, a flavor of Arduino microcontroller for wareable technology. Little LEDs light up in the decoration based on how many #prolife hashtags are posted on Twitter over a specified period of time. Knight links the projects to many linages of practice.

The adoption of decorative textiles, an artform fogged by traditional modes of feminitity, for a politically potent topic is one example. Leaving the application stitching visible by using a contrasting color with the reproductive organs “can make explicit the workings of a circuit” more visible by implication, implying “polarity, connectivity, and flow” that most digital hardware products obscure. A key aspect emphasized in the wearing this visualization on the body is to “remov[e] data visualization from the screen or page…relocates discourse around reproductive justice onto the site of legislative inscription—the body that may be affected by pregnancy.” If you read the article linked above, you’ll be treated to a thick fabric of theory and practice that legitimizes what could be glossed as an non-academic pursuit if you aren’t considerate or don’t read the whole article. As it relates to an expansive sense of data, we can appreciate an embodied experience of data that has it’s origin in technology on the screen, but seeks to escape it. Once can imagine a version of this wareable, perhaps untenable for long periods of usage, that vibrates when a word from a hashtag dictionary is posted. Imagine that as an embodied experience.

Writing about a large sense of what data can mean in a digital humanities project leaves to invigored by how many potential new modes and methods are left underexplored in a field. Digital humanities, in whatever manifestation or metaphor of organization, seems to experimentation and not wedded to received ideas of scholarship, which I find appealing.

            I’ve always found it amazing how retail stores can accurately predict to the hour of the day when a package you have ordered from them will arrive. With so much human activity Involved it blew my mind that it’s almost always spot on. Although for this to happen though, I knew there was so much exploitation in the works. Amazon drivers and workers working insane hours just to drop off your package on time is something that has sickened me for a long time but, “See No Evil” by Miriam Posner, opened my eyes to a deeper problem. This article dives into the tech infrastructure and also the human infrastructure of what makes up a supply chain. This article is a huge realization to me that there is a level of deception and exploitation that realistically nobody can comprehend.

For most companies, supply chain activities are sorted and managed through a software suite called SAP (Systems, Applications, and Products).  SAP is a massive software that companies can purchase to take care of all your supply chain needs and much more. Without the need to create and code software of their own, this cuts out a massive project that would take an absurd amount of time to do themselves. Products like these exist all over the business world, Salesforce and AWS for example do different things but, create a backbone to your business needs. Yet, companies are using these prewritten software’s without any real knowledge of what is going on behind the scenes. With SAP handling everything for you in this massive waterway of suppliers in your supply chain,  it becomes almost impossible to know where every product is coming from or how it’s being produced. Leonardo Bonanni put it like this, “If you’re a small apparel company, then you still might have 50,000 suppliers in your supply chain. You’ll have a personal relationship with about 200 to 500 agents or intermediaries. If you had to be in touch with everybody who made everything, you would either have a very small selection of products you could sell or an incredible margin that would give you the extra staff to do that.” Thus, making it nearly impossible with the current system to verify the working conditions of those creating the raw product or even verify that it is coming from where your company says it is coming from. To think that even companies are mostly in the dark when it comes to how workers are being treated while making their product at some point in the supply chain, is a scary thought and makes you wonder just how bad it can get.

So what can we do to fix this? Some ideas like putting it on the blockchain or using machine learning to stay clear of red flagged suppliers are some of the ideas the author talks about. In my personal opinion, blockchain tagging does seem to be the most reliable and ethical option. While I am a skeptic of blockchain and crypto with where it is at now, I do believe that one day we will head to a more online world and these will be the reliable way we operate as a society. With a Blockchain based supply chain, we would be able to tag and reliable tell where product is coming from and where it currently is in the process. With machine learning, I fear that rather than helping the problem, it will help businesses effectively make more money by only working with suppliers that do exploitation really well and can move product fast without hiccups from internal or external events. Rather than making working conditions better and verifiable, it could quickly lead to the complete opposite.

With supply chain methods and practices being so well established in today’s world, is it even possible to just start over or would that lead to total chaos?

I was very excited to participate in the Intro to Educational Game Design seminar on 10.28.21 as I was once an avid gamer. To date, one of the life moments I am most proud of is winning the NBA Street Vol. 2 tournament hosted at my college freshman year. I still brag about that to my college friends to this day. Yes, I am serious.

As the facilitator, Zachary Lloyd, mentioned, games provide “intrinsic rewards”… because they are meant to be a fun experience. When gaming, even losing sometimes can be fun if one feels they are progressing in some meaningful way, or there is a competition factor. I can vividly recall how bad I was first time I played NBA Street and how one of my friends said I was horrible, and he was right…at the time. However, games unlock a competitive desire. I started to play more not only because I thoroughly enjoyed the game, but also because bragging rights are social capital, and social capital is valuable in the right settings.

Zachary discussed the distinction between gamification and game-based learning in an educational setting. My understanding of the distinction between the two is that gamification embeds gaming into traditional pedagogical approaches, and game-based learning is teaching that is tailored around games that are specifically designed for educational purposes.

Zachary also highlighted 5 of James Gee’s (2013) principles of game-based learning:

1. Identity
2. Situated meanings
3. Well-ordered problems
4. Risk taking
5. Pleasantly frustrating

Of these, I consider risk-taking and pleasantly frustrating to be the most important principles used in games in an educational setting. Because the stakes are generally low in games, students can take risks that have minimal consequences which, in turn, encourages deeper and broader exploration. Also, it is ok to be frustrated by a game as long as the student is learning and funneling that frustration into advancement and productivity. Games are great in this sense because, by nature, they are somewhat detached from the seriousness that comes with typical curriculum but can still contribute to learning. Failure in a game likely doesn’t feel as bad to a student as failure in some variety of formal assessment. Moreover, the concept of gaming tends to imply one will receive multiple chances at success, whereas traditionally learning typically gives you one chance. In essence, something about calling an activity a game implies it is ok to be wrong. Additionally, both gamification and game-based learning can unlock a competitive desire that did not previously exist in the classroom—where individual performance can oftentimes be siloed, and students do not necessarily know where they stand with respect to their peers.

We wound up spending time experimenting with two games from the 1990s, Oregon Trail and Rex Ronan: Experimental Surgeon. This section of the presentation brought back memories of sitting in class in elementary school playing Oregon Trail for a full period (I suspect this was neither gamification nor game-based learning back then). Interestingly, by the time I reached middle school, I thought I hated role-playing games (RPGs), but I eventually grew to love them. In the educational setting, I cannot think of any game genre better for sparking intellectual curiosity and imagination.

            On Wednesday, November 17^th, I attended the Intro to GIS workshop that was hosted by GC Digital Initiatives. Through this workshop, they hoped to teach us three main things. The first being how spatial data is formatted, the second being how to look for spatial data, and the third how to use that spatial data to create a map.

            To start, they asked us to install QGIS if we have not already. QGIS is a mapping software that is great for creating static maps (maps that don’t move). While QGIS is just a software, the GIS in the name means Geographic Information Systems. They defined this as a framework to capture and analyze spatial and geographic data. In the real world, everything you see is all one layer- the roads, the mountains the buildings are all on the same layer but in the GIS world, every feature is a different layer. The waterways, the state lines, and elevation are all on their own layer and stacked on one another. These layers are created by spatial data. Spatial data has at least two dimensions, XY, and sometimes a third, which is Z. There are a few different forms of spatial data. Two very popular versions are vector and raster data. Vector data often represents points, tracks and roads, and land boundaries. Raster data is often used as classification data and changing values through space such as altitude, temperature, precipitation and population density. As the hands on part of the workshop, we were asked to load in the New York City zip codes. Once we did that, the next goal was to highlight the zip code you live in. For me, I live in the financial district so, the zip code I highlighted was 10005.

            As someone who is just getting into the GIS world but hasn’t done much with it since last spring semester, attending this workshop was a nice refresher and it made me want to go back and relearn some things. Such as map projections and understanding the many different ways spatial data is stored. These are very important concepts to comprehend if you want to truly grasp how GIS works.