Bibliographic record
Abstract
The work of Sara Ahmed and Judith Butler exemplifies a recent concern with the politics of affect. Their distinctive contributions are informed by phenomenological accounts of passivity and agency. They view affect as critical to the articulation of social and political space, as well as to the individuation of embodied agents; for each, affect is key to an account of critical engagement. Their at-tention to affective economies also reflects their concern with the dynamics of exclusion, concealment, and marginalization, and thus their powerful insights into the politics of affect contribute to our understanding of the role that affect plays in both the formation of normative orientations over time, and also to their potential disruption and transformation.Le travail de Sara Ahmed et Judith Butler illustre une préoccupation récente pour la politique de l’affect. Leurs contributions distinctives sont éclairées par des récits phénoménologiques de passivité et d’agentivité. Elles considèrent l’affect comme essentiel à l’articulation de l’espace social et politique, ainsi qu’à l’individuation des agents incarnés; pour chacune, l’affect est la clé d’engagement critique. Leur attention aux économies affectives reflète également leur préoccupation pour les dynamiques d’exclusion, de dissimulation et de marginalisation, et ainsi leur puissante connaissance de la politique de l’affect contribue à notre compréhension du rôle que joue l’affect dans le modelage des orientations normatives au fil du temps et leur perturbation et trans-formation potentielles.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".