Article court: Les enjeux d’accès aux terrains criminologiques au Nouveau-Brunswick
Bibliographic record
Abstract
Cet article explore les défis associés à l'accès aux terrains criminologiques au Nouveau-Brunswick à travers deux projets de recherche distincts. Le premier projet examine le travail du sexe dans cette province, alors que le second analyse les enjeux de cohabitation sociale à Moncton, l’une ville des villes à la croissance la plus rapide au pays. Le Nouveau-Brunswick, une province majoritairement rurale et l'une des plus pauvres du Canada, présente des défis uniques pour les chercheurs en criminologie, en particulier en ce qui concerne le recrutement des personnes participantes et l'accès aux données. Les obstacles incluent la sensibilité des sujets étudiés, les difficultés géographiques, et la méfiance des parties prenantes. Ces défis ont nécessité l'adoption de stratégies méthodologiques innovantes pour surmonter les obstacles et obtenir des données fiables. L'article met en lumière l'importance d'adapter les approches de recherche aux contextes locaux et de répondre aux réalités précises des populations étudiées.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".