FCJ-184 Interpassive User: Complicity and the Returns of Cybernetics
Bibliographic record
Abstract
This essay discusses the notions of "extension" and "prosthesis" as two different logics and modes of being with technology.I trace the two terms to the work of Marshall McLuhan, influenced by the work of Norbert Wiener and Buckminster Fuller.I argue that the logic of softwarisation (Manovich, 2013) is similar to the logic of extension, while the logic of appification (IDC, 2010) is similar to that of prosthesis.I argue that these logics also map onto the logics of metonymy and metaphor.I explain why such a distinction is useful for reading mobile apps and the computing practices they enable.I conclude by raising questions about users' complicity within the bio-technological cybernetic assemblage: What does the user of these technologies want?Is she able to confront her desire through their use?Why is the demanding swarm of parasitic 'media species', such as apps, so determined to get under the user's skin?issue 25: Apps and Affect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".