La relecture sensible : vers une conscientisation du « savoir‑lire éditorial » ?
Bibliographic record
Abstract
S’il est en plein essor aux États‑Unis, le métier de sensitivity reader est encore marginal en France. À partir d’entretiens semi‑directifs menés avec 14 professionnel·le·s concerné·e·s (relecteur·trice·s sensibles, éditeur·trice·s, agent·e·s littéraires), nous proposons dans cet article de mieux cerner les spécificités de cette activité émergente du point de vue de celles et ceux qui sont amené·e·s à l’exercer ou qui la promeuvent. Nous montrons les interrogations concernant l’émergence d’un « savoir‑lire éditorial » spécifique, où le vécu discriminatoire et l’affirmation d’un « savoir situé » sont au centre de l’expertise. Nous concluons sur le rôle d’intermédiaire de l’éditeur·trice, qui cherche à promouvoir une pratique encore invisibilisée dans les maisons d’édition.
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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 | 0.031 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".