Bibliographic record
Abstract
Le crime est l’unité d’analyse de base des approches classique et néoclassique en criminologie. Cet intérêt pour l’acte délictueux a été mis de côté pendant une bonne partie de l’histoire de la criminologie, mais a été remis de l’avant avec la publication, en 1968, d’un texte marquant de Gary S. Becker. Le présent article vise à décrire la place du crime parmi les contributions publiées par la revue Criminologie depuis sa fondation, la même année. Après un bref rappel des grands principes de l’approche néoclassique, il est démontré que le crime a occupé une place marginale dans la revue ; les onze articles ayant spécifiquement analysé une forme de crime sont ensuite décrits de façon à déterminer les points communs entre les articles. L’article se conclut avec quelques remarques et observations sur l’avenir de la recherche sur le phénomène criminel diffusée par la revue Criminologie.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".