Bibliographic record
Abstract
Je ne me rappelle pas comment cela a commencé. Mais Andrée Lajoie et moi avons toujours entretenu le plaisir de la conversation. Sur les choses de la vie comme sur le droit. Pour lui rendre hommage dans ces Mélanges, j’ai imaginé une conversation que j’aurais pu avoir avec elle sur la pénalisation du tabagisme, un prétexte pour écrire sur l’émergence de normes pénales. Et pour relier cet échange imaginaire à des réflexions de sa propre recherche. En boutade, elle m’a souvent affirmé ne rien connaître au droit pénal. Je sais déjà que mes propres analyses sur l’émergence de normes pénales peuvent se rattacher à ses positions théoriques sur le droit. D’autres exposés exigeront peut-être de ma part plus que de la persuasion rhétorique pour la convaincre du bien-fondé de certains points de vue.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".