PIECES : un projet alliant recherche et intervention dans un processus d’innovation ouverte
Bibliographic record
Abstract
La recherche collaborative, c’est-à-dire la collaboration entre la recherche et l’intervention, est une manière innovante de faire de la recherche. Celle-ci vise l’adoption d’un rapport plus symétrique entre les chercheuses et les chercheurs avec les personnes intervenantes, favorisant la cocréation des savoirs théoriques et expérientiels contribuant ainsi à l'avancement des connaissances dans de nombreux domaines. Le Projet intercollégial d’étude sur le consentement, l’égalité et la sexualité (PIECES) s’est articulé à travers les principes et fondements de la recherche collaborative. PIECES offre une illustration tangible de la manière dont la recherche collaborative peut constituer un instrument efficace pour traiter des problèmes sociaux complexes, notamment les violences à caractère sexuel dans les environnements collégiaux. Les différentes phases du projet, passant de l'enquête à la sensibilisation, mettent en évidence la variété des approches envisageables, les éléments facilitants et les défis et enjeux associés à la recherche collaborative.
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 | 0.080 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".