Bibliographic record
Abstract
Cet article porte sur l’élaboration du risque de cancer du sein en France et sur ses conséquences sociales. Il s’appuie sur des données historiques et sociologiques pour tracer l’évolution de sa prévention, de la fin du xix e siècle aux tests contemporains de prédisposition génétique. Il montre que la détection précoce s’est imposée comme la technique d’excellence de prévention du cancer du sein, dans un monde où les thérapeutiques évoluent peu en matière de guérison, d’une part, et dans lequel les discours alternatifs n’ont eu que très peu d’audience auprès des publics concernés, d’autre part. Basée exclusivement sur le dépistage, l’entreprise française de prévention du cancer du sein a progressivement transformé des personnes en bonne santé en patientes asymptomatiques — dans un processus de médicalisation du risque — et des populations ciblées de femmes en population à risque — dans un processus de naturalisation du même risque. L’entreprise de surveillance qui organise cette situation est l’oeuvre d’une autonomie médicale qui légitime un « faute de mieux », en l’absence de remède efficace.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".