Bibliographic record
Abstract
La formule usuelle pour definir la criminologie est la suivante : science qui etudie le crime. Le premier probleme de cette definition est de faire oublier la construction juridique du crime et les contextes d’application de la loi. Le second probleme de cette definition est d’enfermer la criminologie dans les objets de cette construction juridique quand l’histoire de ce champ d’etudes et son activite de connaissance les depassent largement, portant sur la question des inegalites sociales au regard de la justice, sur les droits de la personne, sur la justice sociale, la question autochtone, les diverses extensions de la surveillance, les criminalites internationales, etc. L’objectif de ce livre est de comprendre ce que fait la criminologie par l’illustration de ses savoirs sur des problematiques particulieres au Canada. Ainsi, il est d’abord et avant tout une initiation aux grandes questions de la criminologie, l’ancrage territorial servant davantage a faire vivre les apports des savoirs criminologiques. L’enjeu n’est toutefois pas de couvrir tout le champ de la criminologie, mais bien de donner le pouls des grands axes sur lesquels elle s’est developpee pour comprendre l’evolution de ses questionnements. Enfin, ce livre se veut un echo de la mouvance des questionnements en criminologie pour mieux en comprendre les developpements jusqu’a ce jour.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".