- Does the paper have a specific title, thesis and blueprint along the lines of what we’ve discussed in class?
- Does this paper have a compelling thesis/blueprint? Is the thesis debatable amongst reasonable people?
- Is the paper clearly and logically organized from sentence to sentence and from paragraph to paragraph?
- Are all claims well supported with outside evidence, quotes etc.?
- Does the paper avoid making general claims about all humans, people, society, time, space, place, technology, children, adults, etc.?
- Does the paper cite appropriate, scholarly sources?
- Is the paper clearly and artfully written?
First, what is a “networked public sphere” and what are networked “counterpublics”?
Second, how are both shaped, for better AND for worse, by social media networks?
Third, how is the 2020 U.S. election playing out on social media networks and how is the election intersecting with the public sphere online?
Fourth, review what “slacktivism” is in the reading by Tufecki and discuss the extent to which slacktivism is and is not relevant to describe online electioneering/online engagement with the election. In other words, while Tufecki provides a compelling story of what’s possible with online activism, what might she be neglecting in the American context?
Fifth, how does Siva Vaidynathan’s account of Khalid Said in Antisocial Media differ from the account Tufecki provides on pages 22-24 and how does it differ from the notion of slacktivism?
Sixth, what is “techno narcissism” according to Vaidynathan and how is it both ethnocentric and imperialistic?
With members of your group,
- Read and discuss all the information you can find on your assigned network to make sure you understand how it works. Prepare to present your findings to the class.
- Discuss what’s at stake with this network – what argument does it make about contemporary networks and/or the internet? What is it trying to intervene in? Or, what is the network trying to accomplish? Prepare to present your findings to the class.
- with this book, Noble is trying to bring together an analysis of how capital, race, and gender all work together in the context of online algorithms to perpetuate oppression
- by “capital” she means it in the sense of economics – anything that’s an asset (usually money but not necessarily) and that can enhance your power to perform economically useful work.
- in a Marxist understanding of political economy, capital is money used to buy something only in order to sell it again so that you can get a profit – for example, capital is the money that people in Boulder use to buy houses, fix them up and sell them for profit
- so it’s important for you to understand right at the outset that this book is not just or not merely about how online algorithms work – it’s about how they are connected to global systems of wealth accumulation as well as race and gender; none of these things, for Noble, are independent from each other
- this is the essence of an intersectional approach – here, applied to algorithms
- specifically, Noble spends most of the book examining the commerical co-optation of Black identities, experiences and communities in one of the largest and most powerful tech companies in the world right now – Google – and she does this partly to give us tools to stop looking at things like algorithms as anything but neutral
- instead, racism and sexism are part of the architecture and language of technology
- it’s an interesting and maybe more sophisticated version of the argument McLuhan staged in “The Medium is the Message” between him and General Sarnoff; again, you remember that Sarnoff believes that technology/media are neutral – it’s what people do with them that matters; by contrast, McLuhan believes there are certain in-built AFFORDANCES in tech that encourage some things and discourage others
- Noble believes the same as McLuhan but she puts a lot more responsibility not on consumers or users but on MAKERS of technology and media who build their own biases into their productcs which is not a distinction either Sarnoff or McLuhan ever made
- she’s also writing as an encouragement for us to start thinking about the value of breaking up and regulating large tech monopolies and about the value of “the public” and public ownership of information and information retrieval systems
- not just the value of public libraries, for example, but the value of having a realm in our lives that has nothing to do with profit or capital or neoliberalism
- on that note, that term “neoliberalism” appears a lot throughout her book and I want to make sure you all have some understanding of what it means
- it’s very basically a way of thinking in economics, specifically from the University of Chicago, that came about in the 1950s
- It’s used to refer to an economic system in which the “free” market is extended to every part of our public and personal worlds.
- The transformation of the state from a provider of public welfare to a promoter of markets and competition helps to enable this shift.
- Neoliberalism is generally associated with policies like cutting trade tariffs and barriers.
- Its influence has liberalized the international movement of capital, and limited the power of trade unions.
- It’s broken up state-owned enterprises (think about highway 36, about internet providers, healthcare, etc), sold off public assets and generally opened up our lives to market thinking.
- one of the many problems people see with neoliberalism is that a way of thinking about economics has somehow, just like Google, become so naturalized and invisible and unquestioned and so much like air that we can’t see how it’s works on very aspect of our lives, and not just the realm of our lives concerned with finances and money
- finally, I want to be really clear on something I brought up a week or two ago: think back to our discussion of Donna Haraway and the Cyborg
- we want to be really careful to not slide into a way of thinking that says “this side is good and that side is bad” or “this person is right and that person is wrong” – instead, what I want you to be thinking about and focused on is “what are the stakes for making that assertion? who gains power and who loses it when that binary (see page 15) appears as natural, timeless and true on the world’s most popular portal to information?
- like so many things in the world of math, the development of algorithms has a long history going back to the middle east.
- The word itself comes from the name of ninth-century mathematician – Muḥammad ibn Mūsā al-Khwārizmī, Latinized as Algorithmi
- he was a Persian scholar who worked in mathematics, astronomy, and geography. Around 820 AD he was appointed as the astronomer and head of the library of the House of Wisdom in Baghdad
- ‘algorism’, then, originally referred to any arithmetical operation using Arabic numerals, before shifting in meaning to its current sense in the late nineteenth and early twentieth centuries.
- The works of Leibnitz, Babbage, Lovelace, and Boole are full of procedural and proto-algorithmic notions of language, logic, and calculation
- Nowadays the term algorithm is most commonly associated with computer science but it generally refers to any procedure that reduces the solution of a problem to a predetermined sequence of actions.
- In software, algorithms are used for performing calculations, conducting automated reasoning, and processing data (including processing digital texts – this is what a good portion of the Digital Humanities all about)
- but algorithms may also be implemented in mathematical models, mechanical devices, biological networks, electrical circuitry, and practices resulting in generative or procedural art
- algorithm also usually refers to a specific kind of algorithm called a “deterministic algorithm,” meaning a finite and generalizable sequence of instructions, rules, or linear steps designed to guarantee that the agent performing the sequence will reach a particular, predefined goal or figure out that the goal is unreachable.
- The “guarantee” part of this description is important because it differentiates algorithms from heuristics, which usually work by pragmatic reasoning or rules of thumb
- Like algorithms, heuristic methods can be used to reach a desired end state and may be responsive to feedback from external sources.
- However, heuristics are really about informal trial and error rather than constrained, formal algorithmic activity according to a set of predefined rules
- Almost any everyday problem can be solved heuristically or algorithmically
- for example, we use heuristics when we’ve lost our car keys: I look in my bag. I look in my bag again. I search my jacket pockets. I check the front door, because I left them dangling there last week.
- The weak point of the heuristic method starts to become visible when its user needs to shift gears. I’m not finding my keys in the usual places. Should I peer into the locked car or check the washing machine? Is it possible someone has taken them? Should I keep looking, or is it time to give up and call a cab?
- The basic problem with heuristics is how to decide half-way through the process what would be an appropriate next action – in other words, how to design heuristic rules that lead to good solutions instead of bad ones
- If heuristics fail or turn out to be too unsystematic, we can shift to algorithmic problem solving (expressed here in pseudocode): WRITE ON BOARD
For each room in the house,
and for each item in the room;
pick up and examine the item.
If the item appears by objective criteria to be the missing object,
terminate the search.
If not, put down the item and continue this loop
until all items in all rooms have been tested.
- Eventually, if this algorithm is executed perfectly, we will either find our keys or determine conclusively that they are not in the house.
- It requires no ingenuity on the part of the performer because we know to expect one of two prescribed outcomes before even undertaking the search.
- Also, importantly, this algorithm is almost wholly generalizable.
- If you suspect you have left your keys at a friend’s house, you can run the process there.
- If the misplaced object is a watch, or a hat, these steps are equally applicable.
- However, as we’ve been talking about in class, it is also important to acknowledge that even the most clinically perfect and formally unambiguous algorithms embed their designers’ theoretical stances and also biases toward problems, conditions, and solutions
Details now available for that amazing extra credit event I mentioned in class! But you must register beforehand here. His design of a graphical user interface for operating systems was used in Apple’s Mac OS and later in Microsoft Windows. But he’s done a whole lot more that you can see here.
Mon, November 11, 2019
4:30 PM – 5:30 PM MST
Roser ATLAS Center, Cofrin Auditorium (ATLAS 100)
Hi all, if you’re planning on writing about anything to do with fake news, disinformation, misinformation, algorithms, election interference, privacy etc, definitely check this new website MediaWell out that compiles scholarship on these topics from across many disciplines. Here’s how they describe their project:
Disinformation, misinformation, and “fake news” are longstanding phenomena that, in the wake of the digital revolution, have become newly politicized and consequential. Citizens around the world have instant access to a vast variety of information – some of which is purposely misleading or manufactured for political ends. The known uses of disinformation include coordinated campaigns aimed at influencing elections and undermining democratic processes. In response to these developments, new research on mis- and disinformation is rapidly emerging from a range of academic disciplines.
MediaWell is an initiative of the Social Science Research Council that seeks to track and curate that research. As a respected, nonpartisan, and cross-disciplinary organization, the SSRC is uniquely placed to help consolidate an expanding scholarly literature that originates in multiple, partially overlapping fields. As part of this initiative, we summarize research findings, identify gaps in scholarship, contribute to policy decisions, and translate academic knowledge for a broad audience of scholars, journalists, and interested citizens.
This is a fascinating and helpful piece that just came out a couple days – and exactly relevant to things we’re talking about in class: “When Binary Code Won’t Accommodate Nonbinary People: The next frontier in gender rights is inside databases.“
1) What is a predictive model? How does it work in an app or a piece of software? How is racism like a predictive model, according to O’Neill?
2) What are the three elements of a WMD? What, then, is the connection between the reading you did from The Blackbox Society and predictive models in apps/software?
3) Choose an online platform for your group to analyze. Have each member of your group access the platform and compare experiences from one person to another (looking at either search results or targeted advertising). See if you can reverse engineer the predictive model for each person in your group and discuss the underlying assumptions and larger implications of the predictive model.
- What is the difference between a primary source and a secondary source? Should a research paper include primary sources, secondary sources, or both?
- What is an example of a nonacademic source and what do you use these nonacademic sources for?
- What is the difference between a library catalog and a database? Name some databases relevant to our class.
- What is the difference between Chinook and Prospector and Interlibrary Loan?
- What is the difference between subject word searching and keyword searching?
- What does “peer reviewed article” mean and why do you want to include these sources in your papers?
- Which literature-related databases are full text? What IS “full text”? Which database is the most complete and extensive for doing literary research?
- When you’re doing an advanced search, what function does the Boolean operator “and” serve?
- When you’re doing an advanced search, what function does the Boolean operator “or” serve?
- When you’re doing an advanced search, what function does the Boolean operator “not” serve?
- When you’re doing an advanced search, what function does the Boolean operator “*” or “?” serve?
- When evaluating a source for a research project, what aspects of the source should you consider?
- What do you need to do to conduct research on your computer at home/off-campus?