Quand toutes les voix ne sont pas pareilles : le défi particulier que posent les consultations sur le racisme et la discrimination systémique
Bibliographic record
Abstract
Dans cet article, il est question des expériences québécoises récentes de consultation publique en lien avec la diversité et la discrimination. Après avoir examiné les questions qui sous-tendent toute initiative de consultation de ce genre, notamment en ce qui a trait aux défis méthodologiques et au problème de la légitimité socialement reconnue aux participants, nous prenons l’exemple d’une consultation locale menée par une coalition d’organismes auprès de la population d’origine latino-américaine afin d’illustrer le discours que les victimes de discrimination parviennent à partager dans un environnement sécuritaire, mais aussi les limites que ce type de démarche entraine sur le plan de la réception de leurs témoignages. Nous concluons avec une réflexion au sujet des limites que la représentation sociale du « nous » québécois et de son altérité impose sur la capacité collective de confronter les injustices consubstantielles aux relations majoritaires-minoritaires.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".