Bibliographic record
Abstract
Les régimes de visibilité et d’invisibilisation, du cacher et du montrer, sont particulièrement significatifs en matière de mort animale. S’il est fréquent de considérer l’abattage comme une ellipse entre l’animal et la viande, des pratiques comme le sacrifice ou la chasse se présentent à l’inverse comme nécessitant de rendre la mise à mort visible et attestable. Au statut de morts « bonnes à voir » ou « à cacher » s’ajoute aujourd’hui la question du traitement médiatique de la mort des animaux, dont les images possèdent un impact puissant, à l’instar des vidéos des mouvements animalistes et des reportages télévisés. L’expérience de la mort animale affecte aussi la chercheuse ou le chercheur, amené lui-même à interroger son propre rapport au fait de voir et montrer la mort animale. Au croisement de réflexions issues de nos pratiques scientifiques et d’exemples tirés d’une production audiovisuelle, littéraire et artistique foisonnante sur ce thème, nous nous interrogeons sur les différents régimes d’image des morts animales, sur les rapports entre le visible et l’invisible, le montrable et le caché.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Essay on regimes of visibility of animal death; the reflexive note about the researcher's own gaze is secondary to an object that is cultural representation, not research practice.
The object is the visibility of animal death in media and cultural practice, not research practice.
Social analysis of visibility regimes around animal death; not about research as object.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".