Au sujet du besoin d’un niveau de preuve robuste pour évaluer le risque
Bibliographic record
Abstract
La méthode quantitative d’évaluation des risques sanitaires caractérise le risque sur la base de relations de cause à effet établies scientifiquement et d’une quantification de l’exposition établie par des mesures ou issue de modélisation. Dès lors, prouver l’existence d’un risque est directement lié à la disponibilité d’études scientifiques et/ou la capacité des experts à dégager un consensus. Cependant, cette démarche linéaire repose sur la possibilité d’établir les preuves du risque, ce qui s’avère délicat pour certains risques émergents ou nouveaux risques. Non seulement les connaissances scientifiques disponibles ne favorisent plus une prise de décision, mais elles sont parfois à l’origine de controverses. La subjectivité des experts s’introduit alors parfois de manière forte dans l’évaluation du risque.
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 | 0.143 | 0.389 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".