Bibliographic record
Abstract
In addressing the issue of harmful bias in AI systems, this paper asks for a consideration of a generatively wild AI that exceeds the framework of predictive machine learning. The argument places supervised learning with its labeled training data as primarily a form of reproduction of a status quo. Based on this framework, the paper moves through an analysis of two AI modalities—supervised learning (e.g., machine vision) and unsupervised learning (e.g., game play)—to demonstrate the potential of AI as mechanism that creates patterns of association outside of a purely reproductive condition. This analysis is followed by an introduction to the concept of the technology of the surround, where the paper then turns toward theoretical positions that unbind categorical logics, moving toward other possible positionalities—the surround (Harney and Moten), alien intelligence (Parisi), and intra-actions of subject/object resolution (Barad). The paper frames two key concepts in relation to an AI in the wild: the colonial sublime and black techné. The paper concludes with a summation of what AI in the wild can contribute to the subversion of technologies of oppression toward a liberatory potential of AI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".