(this is one of my “other format” responses)
Data should be more like recycling. Recycling is FAIR!
(this is one of my “other format” responses)
Data should be more like recycling. Recycling is FAIR!
Ramsay and Rockwell embark on the process of trying to categorize the Digital Humanities in their article. While I found it interesting and thought-provoking, I thought it was lacking in persuasiveness and somewhat in relevance.
First of all, we must define the term “scholarship” that they refer to constantly in the article. Cambridge dictionary defines scholarship as “the qualities, methods, or achievement of a scholar”. In an article mainly focused on semantics and categorization, I find it extremely important to be precise about meanings and relevance. I agree with tbarb in that the article is “awfully concerned with generalities”. I couldn’t help but think, “Why does it matter?” in response to “Is building scholarship?” I wish the author had been more clear about this (they might have, I just couldn’t tell). However, I do agree that these conversations will be more and more important as DH continues to grow in our world.
Overall, I thought the article made some interesting hypotheticals, but I fail to see its relevance outside DH professors trying to secure grants/funding (in which case it might be very important!).
I thought the video “Native cartography: a bold mapmaking project that challenges Western notions of place” was very thought-provoking. There were a few parts I wished he elaborated on more, such as “more lands have been lost to Native peoples through mapping than physical conflict”. I thought his designs were cool, although I don’t know if I would place them in the same category as the maps I’m used to (but maybe that’s because I haven’t learned otherwise). It felt more like a piece of art that told a story. I’m not sure that is the same purpose of satellite view on Google Maps. I think at its core this video showed that there’s a story behind every piece of earth, and that is something that is extremely important to never forget. When we work with data that deals with history, we have to remember that each entry is a part of an individual’s experience, and the human experience as a whole.
I really enjoyed the “200 countries, 200 years, 4 minutes” video by Hans Rosling. It showed an interesting form of data analysis (though it showed its age). I’m not even sure what to call it, maybe AR visuals? I see this type of thing all the time in NFL broadcasts now. I would love to see a similar video made today with the advancements in graphics. Most importantly, I think this video was a good reminder that data can be interesting and fun. I was actually shocked at how many views/how well received the video was. To be honest, I didn’t think him standing behind the graph added a lot of value to the video for me, but it seems people liked it. This is a helpful reminder that accessibility is crucial for any field of research to make progress educating in the public – that human skills are also needed, and that is what the digital humanities hopes to offer.
Elvis Perez commented on youtube, “I’m only watching this video because my teacher assigned me homework on this.” As am I bud, but I think Conservative Developer made a great point in saying “lool same, but it is interesting.” This video definitely got me thinking of how to try out and implement different forms of data visualization myself!
I really liked this article like many of my classmates, even though I did find the writing style a bit distracting at times. It was really interesting and impressive to see what conclusions can be driven from a small amount of data (although we must be wary). The concept of using metadata seems like an extension of what we’ve been calling “distant reading”, and I think I actually find it more interesting that close reading.
I was confused at a few points, like what “PRISM” is. It would have been nice to have an explanation. But for the most part the author does a great job at keeping a flow of logic and keeping the reader engaged. I also learned new terms, such as “adjacency matrix”, that will be helpful in the future. It felt especially relevant because I have been working with matrices this semester in linear algebra. I wonder if I can use and linear algebra in data analysis? The data visualization was also intriguing to me. All in all, I thought this article brought together a lot of concepts from our class in an engaging way!
I liked reading the (unnamed?) paper by Warren et al. The main goal of their project was to digitally create a map of how early modern people were related and interacted. They describe a number of challenges they face in doing this, such as the lack of documented info from some time periods. Although I couldn’t fully understand all of their statistical techniques, I understood their methodology for the most part. One important takeaway was not inferring causation from correlation. In their example, they show how two people could be mentioned by the same person, but could have no ties to each other. They also used topic modeling which we have recently been talking about and working with.
nickbeati talked about “margin of error” in their modeling. They deemed a 7% margin of error from looking at a random 200-entity set and reviewing for accuracy.
I enjoyed reading this because I had never thought of this idea as a research topic. I imagine doing this project in a hundred years for our time period will be much easier!
Text analysis is something we’ve all seen at some point but might not recall. Remember those god-awful word clouds you saw in 2007? Those are just a form of text analysis. Stefan Sinclair and Geoffrey Rockwell explain text analysis at its core and explain a few examples in their paper “Text Analysis and Visualization: Making Meaning Count”. In their first example they talk about one of my favorite Deadspin articles, which used text analysis to show discrepancies in words used to describe white and black NFL players (the worst kept secret in sports).
HaydenC insightfully points out that in the figure provided in the paper, the rate of the word “tough” appearing was about 1.5 times as high for white players compared to black players, but the total usage was about 2 times as high for black players. While black players do make up roughly 70% of the NFL, HaydenC raises a great question – why are so many more words written about black players on aggregate?
I must admit that I forgot the password to the class website, thus I cannot reference another response and had to respond to a comment instead. Remember classmates, whatever you do don’t cite this.
Simpson’s paradox was discussed thoroughly in the article (paper?) by Kievit et. al. that I read for class today. Although I have been introduced to this topic before, I did not know the term for it and didn’t know how prevalent it is. The study about the likelihood of SP in simulated data was fascinating – 1.67% of simulated cases saw the “complementary subpopulations show a sign opposite to the aggregate”. I will absolutely be more on the lookout for Simpson’s paradox in every interpretation of data I come across.
In response to “strategies in prisoners dilemma games” , kdomjan writes, “it’s interesting that they put this under psychology, when it’s most commonly recognized as an econ term (as far as I know)”.
While prisoners dilemma is absolutely a hypothetical to show incentives, it can also be analyzed from a psychological perspective. For example, when put into practice, why do some people not make the “optimal decision”?
