La recherche communautaire pour soutenir l’action au GRIS-Montréal
Bibliographic record
Abstract
Olivier Vallerand, Amélie Charbonneau, Kévin Lavoie & Marie Houzeau Depuis 1994, le Groupe de recherche et d’intervention sociale de Montréal (GRIS-Montréal) réalise des ateliers de démystification de la diversité sexuelle et de genre dans les écoles primaires et secondaires. L’expérience acquise avec le temps et les recherches menées par l’organisme ont permis d’améliorer sa méthode d’intervention, entre autres en outillant ses bénévoles afin que leur intervention mette davantage l’accent sur la déconstruction des stéréotypes de genre. En utilisant l’exemple de l’évaluation qualitative d’un projet de semaine d’activités pour les écoles primaires, cet article présente comment l’organisme utilise la recherche communautaire afin d’adapter son travail aux besoins des populations rencontrées.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".