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 did the 2020 and perhaps even 2021 U.S. election play 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, for next class: 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, for next class: what is “techno narcissism” according to Vaidynathan and how is it both ethnocentric and imperialistic?
Professor Ben Robertson will substitute for me on Friday. He will take attendance and monitor your in class work. He will also make himself available to answer any questions you might have about research papers.
Please exchange drafts of your papers with at least 2 classmates during Friday’s class. Please also use the questions below to guide your assessment. You might want to jot down notes in response to these questions or you can use track changes in Microsoft Word / Google Docs to insert comments and suggestions.
- Does the paper have a specific title, thesis and overview of the argument that will be made along the lines of what we’ve discussed in class?
- Does this paper have a compelling thesis? 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?
With the 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.
Group 1: Plant to Plant Protocols
Group 2: Subnodes
Group 3: Run Your Own Social
Group 4: Contra Internet
Group 5: netless
Group 6: deaddrops
We’ll be having a lovely two-day workshop on alternative networks with libi striegl Wednesday Nov. 3 and Friday Nov. 5. See you there! 1320 Grandview Avenue, LOWER LEVEL (entrance is down the stairs on the left / east side of the house).
- like so many things in the world of math, the development of algorithms has a long history and goes 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 (among others) 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 can refer 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,” formally defined as a finite and generalizable sequence of instructions, rules, or linear steps designed to guarantee that the agent (HUMAN OR MACHINE) performing the sequence will reach a particular, predefined goal or establish that the goal is unreachable.
- The “guarantee” part of this description is important because it differentiates algorithms from heuristics, which usually work by “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.
- take a moment and think about what biases have been embedded in the algorithm I wrote down on the board
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.