The bandura is Ukraine’s national instrument, and a symbol of its independence and resistance. For my personal connection with the bandura, I’ve been taking lessons for a little over a year now. While Ukrainian cultural heritage is being targeted in the war, I wanted to learn how to play a traditional instrument. Recently, I’ve also inherited my godfather’s bandura. To me, playing the bandura connects me to my Ukrainian roots as well as my Ukrainian-American identity. I always perk up at the chance to share this wonderful instrument with others, since my saying “I play the bandura” inevitably results in the question “Oh, what is that?”
This past year, I went to a concert performed by the Women’s Bandura Ensemble of North America (WBENA), of which my teacher is a member. After the show, she came up to me and mentioned that a lot of the songs they played that evening were sad. That moment clicked for me. I began wondering what songs are commonly played, and why? There are so many different types of songs that can be played on the bandura – modern, folk songs, dumy (epic poems), dance songs, religious songs, etc.
So, for my project, I decided to try to map out each of the songs at each of the WBENA’s performances to dig into the context behind them and see what trends arose. Yes, I picked the WBENA because I am the most familiar with this ensemble, but I’m also interested in doing this project with them because A. They are a North American ensemble and B. they are an all-female ensemble. Most bandura ensembles are all-male, and the bandura is traditionally considered a male coded instrument. By doing my project on the WBENA, I wanted to look into and share these two perspectives.
The project itself is a Wax-based website hosted for free on GitHub Pages. I’ve had some previous experience with Wax and wanted the website to continue on without worrying about costs after its completion. I also like how efficient and lightweight the framework is – from a simple csv, you can populate a series of pages on a given collection. Also, given that this project is based around a set of PDF’s and tags, the project doesn’t need anything too complex. Each program will have a song list, and each song in that list will lead to its own page providing context (such as language, composer, genre, themes, etc) and (where applicable) lyrics and an embedded audio or YouTube clip.
For the project, the team would be working very closely with the WBENA, ensuring that they are 100% on board with what we’re publishing and also turning to them as a resource for our research into the songs.
This project is fairly unique – the only similar website that I was able to find in my search is Songs of Truth, a website for the CD recording of the Julian Kytasty bandura concert of the same name. In this site, however, each song has lyrics (where applicable) and the program notes from the concert on its own page. For my site, I’d like to situate each performance in its own time and see what songs and themes repeat. I’d like for people to come and take a deep dive into the repertoire and discover the rich history and bright future of bandura music and the people who play it.
Back in October, I attended a workshop from GC Digital Fellow teaching attendees how to use pygame. I had previously used RenPy, a Python framework for writing visual novels, but hadn’t had much luck trying to learn game engines such as Godot. Before the workshop, our instructor, Zach, provided us with a GitHub link with the assets we’d need as well as instructions to download the pygame software. In addition to the images we’d need to create our game – a Frogger clone – we’d find a copy of the finished code, which we would use as a reference when building the game from scratch.
On the day of the workshop, Zach patiently took us through developing each aspect of the game, from the basic variables that we’d need to plug in later to a basic class for objects in the game to character movement. Along the way, he would mention additional measures that we could take on our own time, such as sealing off all four sides of the screen from player movement (meaning that the player can’t go “out of bounds” so to speak). I would often find myself detouring to tweak aspects of my game so that I could gain a deeper understanding of how each part of the code worked.
While we didn’t have time to get through the whole game, we had set up each element of the game and had the final code to reference after the workshop ended. At the end, we were challenged to tweak this final code to create a more interesting game, which I wound up doing. I took the challenge to have the treasure chest (the goal to complete the level) appear in a random position after the first level. I had the enemies increase in speed as the levels progressed at different rates, and moving in different directions. I also changed their movement pattern to loop around when they left the screen rather than bounce back and forth. I added a level counter, and had the game loop back to the first level in the event that the player loses. Through the workshop, I had the foundation and freedom that I needed to truly play and explore in the software.
This workshop, which uses free software and shares assets and instructions through a public repository on GitHub, made me think of our previous discussion on Open Educational Resources. The materials are freely available, and even without the guidance of the workshop, people can learn how to get started with pygame. Furthermore, although the project itself starts out fairly linearly, this form of instruction allows the participants the freedom to pursue their own interests with the software. For me, this is the best way to learn programming – to be able to not only ask your own questions, but answer them as well.
In Mary Shelley’s novel Frankenstein; Or, the Modern Prometheus, the titular Victor Frankenstein devotes his college career to manufacturing life. Once he figures out how to accomplish this, he immediately runs out of the house in fear of his own creation. This creation, functionally a newborn, learns from Victor’s neglect and selfishness as well as the fear and disgust of those around him to become a villain to Victor and his family. The creature is not made evil. He instead learns of pain and the desire to lash out as a direct result from the world around him, especially from his creator.
When we think of AI, we think of a brilliant, apathetic, arcane assemblage of math, logic, and technology that can easily outsmart its creator. We imagine HAL 9000, Auto, or Ultron, potentially conscious beings that can turn against us all in the name of following their programming. AI is, in this train of thought, a villain in its own right that we were foolish enough to flip on.
Yet, much like Frankenstein’s Creature, AI isn’t a naturally malevolent force. It also isn’t some objective force inherently better than its creator. It is only as good as the influences that it can learn from. And it learns from us. It needs to be fed data to learn from before it can make any judgments or create any responses, and, as previously discussed in this class, our data is far from objective.
Like the Creature, AI is often seen as this Boogeyman in its own right – solely responsible for stealing jobs and leading to increased oppression. To clarify, AI is absolutely used to do these things, but it is not a sentient force that can be held responsible. As Dr. Lauren Klein points out, corporations and government agencies using AI want it to be perceived as this objective black box to obscure their own goals. Corporations who are looking to use AI to replace jobs would cut down work forces and look for cheaper labor without AI existing. Law enforcement racially profiles citizens with or without facial recognition software. With or without an algorithm, humanity grapples with who owns what information. Furthermore, doctors may not be so inclined to use biased AI note taking software during appointments if they weren’t so bogged down with work from the corporate healthcare model so prevalent in American society. The evils of AI are nothing more than children of the ignorance and greed of those in power.
Conscientious use of AI could result in allowing us to see situations from a different angle (i.e. in the case of Deep Blue or Alpha Go), or to lessen the emotional or physical toll of processing data. On its surface, AI is first and foremost a tool. Unlike the Creature, it doesn’t have an emotional state nor a desire to lash out at the world. It simply does what we tell it to do. If it floods the internet with indiscernible gray text, then, despite the horror stories going into print, that will be the result of people breaking the tap on the faucet for any number of reasons including additional ad space, pure malice, or the innately human desire to simply see what would happen.
The irony in writing this is that I, personally, am most inclined to, like Frankenstein, run out of the house on AI when I can recognize it. I refuse to use AI to generate text, code, or (especially) imagery. For the former, I would like to keep my own skills sharp and make sure that I understand what I am engaging with. For the latter, I refuse to benefit in any way off of the stolen artwork of artists who neither provided consent nor were in any way adequately compensated. With all that being said, even as someone who does her best to avoid AI (and almost certainly fails in some regard), I do believe that we need to analyze the problems with AI at the human source.
When my cousin’s first child was young, he told us that he and his wife would raise their children to be involved in what they were doing around the house. Instead of only play acting cooking, they’d give their kids a cutting board and some vegetables to cut to help with dinner. They’d include their kids rather than simply saying that they were too young. Therefore, by the time that they would be expected to do chores, they would know what they were doing, understand that they were helping, and know that they were encouraged to learn more about the practical tasks of the household. It wouldn’t be a chore so much as something that they were used to doing and knew that they could do.
This week’s readings reminded me of how much the typical American educational experience (focusing on K – 12 here) is a chore. Standardized or otherwise, we mostly teach our children to learn to take tests. Memorize this vocabulary, use these words not those, parrot the point of view I’ve given you, remember some dates and the names of a select few “important” people, etc. You can prove your understanding of the material through class discussion sure, but the bulk of your grade (whether you pass or fail) will depend upon tests and essays, in which you must convince me, the teacher, that you have understood what I told you in exactly the right way. Remember – use a number 2 pencil, make your mark heavy and dark, and don’t go outside the lines.
This approach stifles students’ desire to engage with the subject material. As commenter BMBOD annotated in Kalir and Dean’s Hypothes.is experiment: “If you approach knowledge as something coming from an authority, it is very hard to fathom being able to create it yourself, or talk back to it, even if those platforms exist” (25). As DeRosa would say, students become consumers rather than contributors of knowledge. This consumer identity is especially apt since these students are likely consuming this knowledge from the commercially produced textbooks that they are required to buy, in which one particular narrative in a particular field is commodified and proliferated.
Digital Humanities praxis seems to be the antithesis to this rote approach. Working with firsthand sources at their own discretion allows students to come to their own conclusions with history, literature, art, and other humanities. Open educational resources allow educators agency to build their own curricula rather than depend upon a textbook. Social media and blog assignments, as pointed out by Cordell, allow students to hone their writing in formats that they are guaranteed to use after their primary / secondary / higher educational career ends. Injecting these projects into the course plan would allow students to take agency over their own learning and engage more deeply with the subject material. For an ambitious example, DeRosa’s undergraduate Early American Literature class not only developed their own fledgling textbook, but were told that they were ideal candidates because they understood how undergraduates consumed information. Her students were guided and encouraged to produce their own material rather than taught to demur to a higher authority. They were brought into dialogue rather than taught to recite a monologue.
Working on digital humanities projects further breaks this proliferation of a single narrative by exposing students to multiple viewpoints. Instead of reading a synthesized, universal account of a specific time period, for example, students can learn the particular experiences of individuals. the students can be taught to, as Risam describes, “resist the fetishization of the other” and broaden their perspective.
Furthermore, through Digital Humanities projects, teachers can foster more productive dialogue and instill in their students a healthier relationship with failure. In the prominent American educational model, the teacher is the authority, and failure is to be avoided at all costs. However, in Digital Humanities, whether it’s due to technological (first tier) or human (second tier) error, failure is a part of the process. We learn and develop both our software and ourselves through failure, just as we do in life. Learning how to fix flaws in our code as well as in our own logic are essential to success, both in a digital project and in our personal growth. Furthermore, as Craxall and Jakacki point out, teachers cannot wait until they learn everything about Digital Humanities to step in front of a classroom. As DeRosa points out, disciplines have very short shelf lives – by the time you’ve mastered say, a new software, that software will have a new update that you will have to learn. This learning curve for teachers requires teachers and students to learn together to provide a richer experience.
Injecting Digital Humanities projects into primary / secondary education provides students with digital and informational literacy as well as the confidence to engage and effect change in their local communities without fear of failure.
I entered this Praxis assignment with a goal: measure pronoun usage in at least one Shakespearean play. Why pronouns? I was curious to see how often characters reflected on each other using gendered pronouns. Furthermore, in my research, I found that pronouns were often used as stop words (filler words that are removed before mining – think “the”, “an”, “a”, etc), which made me curious about how much these overlooked words could tell. Why Shakespeare? His plays are easy to find as public domain text files, the plots are well known, several have interesting gender dynamics, and. . . I was in a Shakespeare company in my undergrad years. I figured for new tools I may as well retread familiar territory.
I settled on As You Like It as this play particularly plays with gender – an exiled daughter of a duke disguises herself as a man who then offers to play the role of a woman (herself, no less) to help her love interest cope with his inability to see her. It makes more sense in context. With a woman as the main character, and one whose gender presentation changes throughout the play, I felt that As You Like It would serve as a good test case.
Next, came which software to use. I wound up trying a few, each of which will receive its own section:
Voyant
I started with Voyant, which was definitely the easiest to use. I copied and pasted the link from MIT’s webpage of the script (https://shakespeare.mit.edu/asyoulikeit/full.html) and clicked Reveal.
Immediately, I saw this Cirrus map of the most common words:
The script has 22,817 total words and 3,267 unique word forms according to Voyant with the top five words being Rosalind (the main character), Orlando (her love interest), Celia (Rosalind’s cousin / best friend), love, and good. I scrolled through the most popular words, trying to select both pronouns and gendered terms, but realized that this would be far too manual to be efficient. I then tried searching for pronouns before realizing that this was essentially using the Find function on the original webpage, which also didn’t seem like the best use of the tool. Interestingly, the context tab does show what precedes and follows the term. Unfortunately, the terms only show up when you use the regex term ending in an asterisk, so you do have to filter through words that start with the pronoun (i.e. she -> shepherd). It’s fascinating and provides some additional information, but doesn’t always refer to whom the pronoun is referring. Overall it was a fascinating starting point, but didn’t really answer my question
Google Ngram (Aside)
I pulled up Google Ngram to see if it would help. Considering that it shows how often words appear in a large selection of text, it did not help with my original question. I did want to share however, that out of curiosity, I searched for the term “themself” (single reflexive form of they) as proof of it being a legitimate pronoun / using singular “they” has historical validation. The result? From 1800 – 2022, we see a spike for “themself” usage in the most recent years, which seems to affirm the narrative that singular “they” is a more recent trend.
However, if you were to expand the search to texts from 1500 – 2022, the modern usage pales in comparison to its usage throughout the 16th century. Did this answer my question either? No, but I did find this to be an informative aside.
{L}exos
The last software that I tried was {L}exos, a data cleaning and analysis software from Wheaton College. My original MIT link resulted in unwanted HTML that I couldn’t scrub, so I downloaded a text file of the script from Project Gutenberg and trimmed the front matter (title, Dramatis Personae, and scene list) and end matter (terms of use). Then, I went to the scrub function and tried to: make all text lowercase to avoid case sensitivity, remove digits, and remove punctuation but keep apostrophes. I had to replace the roman numerals for Act and Scene numbers with digits so that I wouldn’t get an improper count of “I”‘s in the text. I attempted to remove all words but pronouns and character names, but the result wasn’t as revealing as I’d hoped. Fascinating, but not revealing. Then, I took out a number of the stop words (i.e. the, an, a, that, enter, exit) to attempt to get a more comprehensive look at word frequency, resulting in this word cloud:
From this cloud, we can see that the first and second person are the most popular, but I still wanted more information. So, I went into the Content Analysis tab and entered text file dictionaries for each pronoun type I was looking for: first person, second person, third person masculine, third person feminine, they pronouns, and themself (out of curiosity). For second person, I included both forms of you and thou. I then selected all of the dictionaries and hit analyze. {L}exos generated a table of how many times each pronoun type occurred – first was first person, then second, then third person masculine, then third person feminine, then they pronouns (sadly, no record of themself).
Interestingly, in As You Like It, the most popular feminine pronoun is “her”, which was used 81 times, as opposed to the most popular pronouns for first person – I: 503 times, second person – you: 422 times, third person masculine – he: 180 times, and they – they: 48 times. “She” occurred in the text 46 times.
Conclusion
Did I (eventually) answer my original question? Yes, thanks to {L}exos, I was eventually able to find out the exact number of times pronouns appeared in As You Like It. Am I surprised that the amount of feminine pronouns was significantly less than first person, second person, or third person masculine? Not entirely – since a play involves characters either monologuing their inner thoughts or speaking with each other, I’m not surprised that first and second person pronouns had such high representation. The fact that the masculine pronouns occurred at roughly three times the amount of feminine pronouns was surprising – though the characters are mostly men, the lead is a woman, and there is quite a lot of pining at the center of this plot. The fact that the most popular feminine pronoun was the objective her rather than the nominative she was in fact surprising.
However, while I was able to find some information, my next step is to dig deeper into whom the pronouns were referring. Is it neat to know how often different pronouns occur and compare them? Sure. Is there a lot of context missing that would make this study even richer? Absolutely. In the future, I’d like to do a web scrape of the script to find who is speaking when each pronoun occurs and at what time so I can go back into the text and analyze who is being referred to. Is Rosalind (/ Ganymede, her male alter ego) referred to more with feminine pronouns or masculine pronouns? How often are each character referred to by pronouns? Do any characters have any interesting balance of pronoun types (i.e. does Jaques have more objective pronouns used toward him than nominative)? I had originally selected As You Like It for its gender subversion. While I answered my literal original question, I feel somewhat unsatisfied in the lack of answer toward the spirit behind my original question.
As I mentioned in class last week, I have relatively recently become a huge fan of the Eurovision Song Contest. Though I do not always agree with the politics of the competition (or the winners selected), I find the kinship, experimentation, and cultural pride to be inspiring.
When I was trying to think of what to map for this project, I figured that it would be neat to see who won each year in Eurovision compared to who would win strictly according to the jury vote and the televote respectively. To put it simply, Eurovision winners are determined by points allocated by the televote (the viewers) and the jury (representatives from each nation participating in the competition. As such, a nation can fail to win the most votes from either or both the jury and televote systems and still win. I was curious to see how often since the joint system was put in place in 2009 all three winners were the same. So, I loaded up Eurovision World (a fan archive of Eurovision contest history) and pulled the data into a spreadsheet.
After considering free ArcGISOnline, I decided to use Leaflet since I have a basic understanding of JavaScript and felt more familiar with writing the code myself. I figured I’d program the map using simple JavaScript and HTML and host the site on GitHub Pages. Also, as someone with Ukrainian heritage, I admittedly felt biased toward the Kyiv-originated open-source site. I pulled up as many tutorials as I thought would be relevant, loaded in a basic tile map, and got to work.
My idea for the map was that the user would see markers, one for each year of the competition from 2009 – 2024, noting the host cities. When the user clicked on one, they would see a popup with the year and the host city name, and then the winners from that year would be colored in. I decided that yellow would represent the official winner, dark blue would represent the jury vote winner, and a sort of fuschia would represent the televote winner. If the official winner also won the televote and / or the jury vote, then the country would remain yellow. I chose these colors for their distinctiveness and also because they roughly matched Eurovision’s 2022 color palette, which was based around those 3 colors.
Adding the markers and popups were easy, and thanks to a Leaflet tutorial, I was able to separate the tilemap layer from the label layer so the markers and color fills wouldn’t obscure the nation labels.
My first big hurdle arrived with adding the country data so I could add in the colorfill since I didn’t want the map to be crowded with markers. In order to fill in each specific country, I would need to pull in geoJSON geometry data, consisting in a collection of points to have the computer draw the borders. After some searching, I found rapomon’s geojson-places module, which contained a function to pull geojson information for countries by two letter country code. Unfortunately, since I wanted to keep the site setup simple, I wound up copying and pasting geojson geometry coordinates for each country into my own geojson file from the downloaded module. This was somewhat time consuming, but ultimately worked. Figuring out how to reliably change the country colors on marker click was a little more challenging considering that the geojson information isn’t inherently accessible after adding it to the map. After much Googling and scouring Stack Exchange, I figured out how to create a Layer Group and change the fill colors per year there.
For finishing touches, I needed to decide where to focus the map. This was especially tricky considering that I not only had to account for Israel being involved in the competition, but also Australia. While these inclusions spark a vigorous debate as to “What is Europe, really?”, I had to decide whether I wanted to show every country on load and have the markers blend into each other more, or focus on most of the countries and have the markers be a little clearer (thanks again for hosting twice in the same location in the past 15 years, Sweden). In the end, I decided on the latter. I also decided to add specifically which country and song won which vote into the Host City popup to aid in map navigation and also include more context behind the win.
Along the way, I found myself adding more details to my to do list – update the markers, add in the other participating countries per year, have each country have a pop up including their song per year – but in the interest of time, I decided to ship the map once my initial plan was done. GitHub Pages also takes a considerable amount of time to deploy the first time, so I definitely wanted to give extra time for troubleshooting in case it failed.
For the current minimum viable product, when you open the map, the popup for the 2009 Moscow competition is displayed, and Norway, winner of all three categories, glows yellow. The idea is that the user will then follow the gold path to the next marker in chronological order, since the winning country hosts the next year’s competition. The only exception is reflected in 2023, where runner up and jury vote winner UK hosted since the winner, Ukraine, could not host due to the war with Russia. The user would have already passed by the one Ukrainian marker at that point, and would ideally be drawn toward the only UK marker in the dark blue filled country.
Some improvements I would like to make:
Add in the participating countries for each year to flesh out who was in the Europe that decided the winners, and lead the audience to ask why.
Change the marker appearance and size. As previously mentioned, Sweden hosted two competitions in Malmö in the past 15 years. Add the Copenhagen competition and you have three markers practically overlapping unless you’re zoomed in on Sweden and Denmark. I was originally also going to change the marker appearance to include the logos from each competition, but refrained due to copyright concerns. Replacing the markers with empty hearts – a nod to Eurovision’s logo – may be a better move.
Perhaps add a line between each of the competitions chronologically to aid the user
Add a legend for the color scheme
Automate the popup population for the host cities
Add in accessibility improvements (i.e. fine tuning the color palette, ensuring I hav ealt text, etc)
Figure out how to remove Crimea from Russia’s geometry
Update the README
Issues inherent to this map:
Pulling someone else’s geoJSON geometry means that I am forced to use their defined boundaries. For example, as mentioned above, Crimea is included in Russia’s geoJSON geometry. I need to learn how to either pull this data myself or edit this data to be more cognizant of contentious borders.
Similarly, hard coding the geoJSON geometry means that this map cannot reflect any changing borders without me going in and updating the coordinates manually.
My premise of “highest jury / televote score” may be slightly misleading – just because a song “won” one of these categories does not mean that they won by a large percent, or even that they placed higher than every other song except for the actual winner. Furthermore, some years the jury vote and televote are at opposite ends of the spectrum, with most of each votes going to their respective songs, and sometimes the highest jury vote or televote counts aren’t that far apart. It would be easy for someone with, say, an anti-jury bias to take a look at the map for the 2023 contest and argue that the jury “rigged” the competition in favor of Sweden over the televote winner Finland when in reality, the televote totals between Sweden and Finland were not far off. My current plan to improve this point is to add in difference in points for that particular category from the other winners.
In our first class, we were challenged to define the Digital Humanities. I described the field with three branches flowing into each other: theory, building digital resources, and integration into pedagogy. While I still argue that these three branches broadly describe the work that occurs in Digital Humanities, this definition is at once too broad and too limited. Instead, Digital Humanities is reflection and production in accordance with its values.
For one, these branches are hardly separate entities. One may argue that the various editions of the Debates in Digital Humanities fall under theory, since the primary goal of the text is to contextualize and present discourse within the field. Through its interactive online publication with the University of Minnesota Press, however, these editions and their authors also fall into the latter categories of building resources and integrating those resources into pedagogy.
Furthermore, while I had originally considered “building digital resources” to be composed of writing code for a particular project, our readings for this week have proved to me that this is in fact not the case. In fact, contrary to Stephen Ramsay’s definition, I would argue that one does not need to know any programming language to be a member of the DH field or to contribute to its existing projects. For example, the articles in Reviews in Digital Humanities are plain text – no programming necessary. Yet, in writing reviews for various DH projects, the authors provide a vital step in the development of these projects – providing constructive feedback and promoting the work for consumption. Both functions are necessary for the project to grow and develop.
After finishing our readings for this week, I agree with Kelly Josephs Baker and Roopika Rasam that the “theory/praxis/pedagogy” models do not suffice in summarizing the digital humanities. They describe the “what” of the work, but not its spirit. Digital humanities is the reflection on and production of digital resources that reflect our humanity. For me, this means that DH does not merely produce and reflect upon digital resources, but its projects must be openly accessible, collaborative amongst authors, authors of other projects, and the communities they represent, and attentive toward the whole human experience, to create my own list of values inspired by Lisa Spiro.
Further Site Reflections
From our other sites for this week, both the Colored Conventions Project (CCP) and the early caribbean digital archive (ECDA)fight the epistemicide (as defined by Fiormante and Chaudhari) of white supremacist history. In providing an archive of digitized primary sources, exhibits, and teaching curricula on the pre-Civil War Colored Conventions Movement, the CCP reasserts the contributions of Black men and in particular Black women in the historical narrative of the nineteenth century fight for civil rights.
Similarly, the early caribbean digital archive both digitizes texts by black, enslaved, Creole, indigenous, and/or colonized people for public access and invites its participants to curate the collection to reclaim and celebrate these narratives. As an example, the archive encourages its participants to help them reidentify authorship to represent who is telling the account rather than the colonist claiming credit. The archive actively transforms the existing historical narrative in the Caribbean by placing the power into its rightful hands. It actively strives for a postcolonial archive, falling into postcolonial DH as mentioned in “The Black Atlantic.”
Finally, Torn Apart / Separidos utilizes Wernimont and Losh’s MEALS system to to highlight the severity of the humanitarian crisis brought about by the US’s 2018 “Zero Tolerance Policy.” Its Materiality stems from the experiences of asylum seekers reflected in publicly available datasets on ICE facilities. Its Values are defending humanity and refuting the set narrative of a nation that would implement this “Zero Tolerance Policy.” The map and its reflections embody the harsh realities implemented by this policy, providing an affect of shock and horror. The labor is undertaken by various scholars, text contributors, the authors of the work researched, and those affected by this tragedy. For situatedness, this project is a product of its time and origin, an openly accessible radical, or political response.
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