Bibliographic record
Abstract
Le détatouage pourrait être compris comme un effacement : effacer son passé pourrait être le moyen de recommencer à zéro, de retrouver une peau d’origine et de donner un sens nouveau à sa peau. Si le sel a pu être utilisé sans succès, les nouvelles techniques font l’objet d’évaluation et d’évolution. Mais les techniques d’effacement sont si invasives et comportent de telles séquelles que le détatouage apparait bien comme un désengagement assumé et risqué : tant du point de vue de l’état de la peau détatouée, qui ne revient jamais à l’état de page blanche sur laquelle on pourrait réécrire immédiatement, que du point de vue physiologique qui voudrait renouveler l’âge de la peau. Nous démontrons qu’il n’y a pas un arrangement avec sa peau mais un « agenrement » à opérer pour redonner un style et un genre à un corps déconsidéré par ce qui serait maintenant un défaut à éliminer.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".