Your project presentation was very interesting to hear about, particularly because of it's continued relevance in today's media. Your use of ArcGis and it's timeline feature was a really interesting way of keeping your data cohesive. I also liked your use of Voyant to help visualize what is actually being said about affirmative action with different keywords and phrases. One small critique I had is that there are several Voyant charts here, and it felt a bit scattered at points. A larger point connecting all of those thoughts would have helped me understand why each graph was important to answer your research questions.

The interpretation of the shift in language about accessibility is a clear signifier of the changing global dialogue. As a student at Vassar, I have heard many complaints from students who cannot access certain buildings. By highlighting what Vassar is intending to do we can hold them accountable as well. Accountability is a huge part of encouraging progress.

This is a super cool collaboration! I love the Loeb. Museum's have access to incredible resources, and I am glad to see them being taken advantage of. Not only that, but museums emphasize the importance of metadata more than many other public organizations. OpenRefine seems like a great tool to keep track of these long datasets.

Language not only tells us a lot about how accessibility is treated by an institution, but is a facet of accessibility itself. Making sure these sources are easy to find has it's own process.

Looking at these cirrus visualizations, there are clear differences in the approach to making Vassar accessible. In the 70s, the largest and most commonly used word is "handicapped" displaying a focus on disabled students. In the 2010s, a focus is placed on the campus instead, and how the campus can instead adapt to the needs of the students.

I think this is a very important issue to be discussing, and am glad to see it brought to our attention in this format. I'm also very interested to see how Vassar and Hamilton specifically interact with ADA compliance and their individual histories with it. However, I feel like there could be larger-scale research applicable here. Vassar and Hamilton are progressive institutions; how might their compliance influence or reflect the larger issue of accessibility in this country?

Your group has chosen a really interesting topic that has very little representation, so I'm looking forward to the final product! As you have acknowledged here, it will be pertinent to study and research the Iroquois as they are a severely marginalized and silenced group in the USA. How will you ensure that you are meeting the ethical standard required to tell these stories?

The idea that social justice content is in high demand feels extremely relevant to this course. It makes me wonder about our own work here, and how we can avoid taking advantage of data for the purpose of perfomative activism. For my blog, I looked at the Holocaust Geographies as one of my selected sources - sadly, the site was not ideal. What does this say about how we treat Holocaust stories and data? I also wonder what kinds of social justice work is involved in promoting accessible websites.

Visualizations and maps are examples of "cooked data". It is difficult to picture, particularly nowadays when Google maps for example might be presented as objective truth, but in reality there is someone compiling and designating the data. In fact, maps are inherently "wrong" because they are attempting to model a three dimensional space on two dimensions. Sizes change, distances are smudged. Additionally, the names of places and zoning might change based on who produced the map and their beliefs. One of the most famous medieval maps, the Hereford Mappa Mundi, looks completely different from the modern globe. It makes me wonder what our maps will look like in another ~700 years.

This is an interesting point, and it makes me think about what exactly the "function" of Voyant is. Are the visualizations meant to provide a clear message on the contents of the book? In my experience, these visualizations are merely tools I can use to form questions about the text at hand. Also, as someone studying education, something like the Bubblelines visualization seem like great in-class tools that students can replicate. That being said, I think what ultimately intrigued me about this post was the notion of "useful" or "clear" information. Data is data, and there could be thousands of ways to utilize it.

I can imagine that cluster analysis and PCA tools are extremely helpful in locating where anonymous texts, diaries, or personal writings may have originated from. Although one of the flaws in computer assisted textual analysis is a underdeveloped catalog of pre-modern language, if we were able to sufficiently input that information it would be much more powerful of a tool. Not only would we be able to geolocate the texts, but sleuth out the gender, race, and more identifying features of the author through the words they use.

I think these are the vital questions being asked when looking at museum catalogs. There was a famous artwork at the Met that read "Do women have to be naked to get into the Met?", emphasizing how few female artists are represented in museums even when nude female bodies are constantly shown off as masterpieces. This is another example of structural bias, where the curators of museums end up curating the knowledge of art of the general population.

Data is presented to us often as objective truth. It is reports like these that remind us that as long as people are the ones compiling data, human error and bias will never cease to exist. If we cannot take data at face value, what are the protocols for consuming data? Perhaps there should be guidelines and tutorials for parsing through data, and that could prevent misunderstandings as the ones seen in the Clery Act data.

Fitzpatrick’s dissection of digital humanities discusses the interesting phenomenon shown here that puts the “critical” and the “creative” processes of humanities work in separate boxes. I would argue that this designation is not a conscious act, and is a result of traditional expectations of the humanities field. Still, I am brought back to Fitzpatrick’s conclusion that digital humanities studies directly opposes dichotomy within the humanities and instead promotes incorporation of media and resources.

This reminds me of a metaphor my high school used to properly explain the difference between equality and equity. Let's say there's a fence, and on the other side is a baseball game that you and your friend are trying to peek over and watch. You each get a box to stand on, and now you can see over the fence! Your friend, however, is shorter than you and still can't reach. Although you may have the same box to stand on (equality), in order to get the same opportunity to watch the game you have to put effort into making sure that everyone actually receives that truly equal opportunity, e.g. another box for your friend.

Costanza-Chock's example of college admissions to explain equality vs. equity also make me think about what kinds of digital barriers exist in place to prevent restorative justice. Issues such as accessibility, class, and status keep coming up for me and now I'm wondering: How does class background influence the attempts made by digital humanities scholars who try to perform this restoration?

Additional contextual information regarding an object is an interesting factor that plays into digital archives. I felt myself asking similar questions when looking at certain objects, such as "what is this objects relevance?" or "is an institution responsible for providing background information?" Ultimately, I think I understand the simplicity of the object descriptions. The Georgia Club photo in the collection had all the information specifically related to the photograph itself, not the context that the photograph existed within. This keeps the photograph within its own boundaries; although it can be used as a source, it also has individual parameters to its existence.

By using GIS, we are able to further realize the scale of displacement such as the one in Africville. Not only does it provide a visual aid, but the map condenses relevant data into one convenient stream. Also, as Kaitlyn says, mapping quite literally returns the residents of Africville to their hometown through their stories, which is an empowering gesture in reaction to the injustices they faced.

“Finding people” as motivation for this project is very inspiring to hear. In my personal life, I have lots of family that perished during the Holocaust, and I know nothing about any of them. It pains me to realize that they have been forgotten, so I am glad to see that there are people trying to avoid that same fate in the case of the White Australia Policy. As written here, the technology is available, it just requires people committed to using it.

Something I often forget regarding the importance of data is that if data is not analyzed or compiled legibly, it can easily be lost. It's tangible, visual form is just as important as the data itself, because it allows equal access to the content.