Bibliographic record
Abstract
This article employs a cyborg and collective view of language (Martins; Viana, 2019) to explore how late Artificial Intelligences operate as devices that contribute to the establishment of racial ontologies and policies of death of racialized bodies. The analysis is based on ChatGPT, the popular Artificial Intelligence created by OpenAI, and the ways in which this device supports and strengthens contemporary neoliberal narratives and politics of enmity. To do so, we will use the concepts of cyborg (Haraway, 2009[1991]) and Actor-Network Theory (Latour, 2012) to complicate understandings about language, as well as the concepts of necropolitics (Mbembe, 2018[2003]) and neoliberal governmentality (Dardot; Laval, 2016) to understand the action of machinic entities today. We argue that these discussions need to be expanded to circumvent, blur and hack ideologies that deal with a precise ontology between humans/non-humans, since it is such imaginaries that allow the production and perpetuation of these very oppressions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".