Bibliographic record
Abstract
Il est intéressant de mener une réflexion sur les médicaments en prison à partir de l’étude du vocabulaire qui les entoure. Ce travail mène à constater qu’il évoque le respect de la loi et la contrainte qui y est attachée. La prescription, synonyme de règle dans le domaine religieux puis médical, renvoie à la question du pouvoir médical. On demande au médecin de prescrire des antidotes à la souffrance carcérale, mais est-ce bien là son rôle ? La délivrance, c’est avant tout la libération, la mise au monde. Mais qu’en est-il d’une délivrance sans libération ? Peut-on être autonome dans la prise d’un traitement si l’on n’est pas libre ? L’observance renvoie ici au contrôle. Dans ces conditions, le médicament en milieu fermé ne devient-il pas une sorte de monnaie d’échange ? Comment faire pour s’assurer que l’usage des prescriptions ne soit pas (trop) détourné tout en préservant la confiance sans jugement que les personnes incarcérées attendent des soignants ? D’où cette ultime question : est-il possible de soigner en prison ?
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".