Weinstock, Jeffrey Andrew. The Monster Theory Reader. Capturer l’image changeante d’une altérité en perpétuelle reconfiguration
Bibliographic record
Abstract
L'ouvrage The Monster Theory Reader, supervis par Jeffrey Andrew Weinstock, dploie les enjeux et thmatiques qui traversent la notion de monstruosit travers vingtquatre articles compils dans quatre parties thmatiques. La "thorie du monstre", tablie sous ce nom par Jeffrey Jerome Cohen, est dfinie par Weinstock comme un champ d'investigation auquel peuvent se rallier des chercheurs de domaines varis. 2 Ces diffrents domaines sont mis en lien thmatiquement dans l'ouvrage. La premire partie offre une bote outils thorique pour les tudes de monstres (Monster Theory Toolbox), en proposant des textes de Sigmund Freud, Masashiro Mori et Julia Kristeva. Et l'ouvrage se clt sur une quatrime partie, The Promises of the Monster (Les Promesses du Monstre), prsentant une srie de recadrages pistmologiques qui nous incitent embrasser la figure du monstre comme une chappatoire aux normes tablies (avec des textes d'Anthony Lioi, Donna Haraway et Patricia MacCormack). Ces articles qui ouvrent et ferment le recueil se distinguent des autres par leur volont de faire du concept du monstre un absolu susceptible d'tre dracin. Les articles rpartis dans les sections Monsterizing Difference (rendre la diffrence monstrueuse) et Monsters and Cultures (Monstres et Cultures) inscrivent quant eux chaque fois leurs monstres dans
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".