Les méthodes qualitatives en « terrain criminologique » : mise en perspective et usage de ces méthodes dans la revue Criminologie
Bibliographic record
Abstract
L’objectif de cet article est de décrire la place et l’usage des méthodes qualitatives tels qu’ils ressortent des recherches qui ont fait l’objet d’articles dans la revueCriminologie. Pour ce faire, nous nous intéressons au développement de ces méthodes à l’École de criminologie de l’Université de Montréal, puis à certaines des transformations qui ont façonné le champ de la criminologie et celui de la recherche qualitative dans leur ensemble et, enfin, aux pratiques de recherches qualitatives diffusées dans la revue. Le corpus retenu comprend 114 articles qualitatifs publiés entre 1972 et 2016. L’analyse permet de dresser un portrait des usages de la méthodologie qualitative, d’exemplifier la façon dont sont rapportés ces usages et de cerner des tendances dont certaines sont communes à celles observées à l’École de criminologie, mais également, en criminologie.
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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 | 0.162 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".