Mieux comprendre la distinction entre les principes d’equite-diversite-inclusion et les approches de decolonisation-reconciliation-autochtonisation au Quebec
Bibliographic record
Abstract
Ces dix dernières années, plusieurs rapports d'enquête et certains événements tragiques ont mis en lumière la nécessité d'agir rapidement dans la mise en œuvre concrète d'approches et de principes fondamentaux pour guider les politiques de reconnaissance. Dans le cadre d'activités professionnelles quotidiennes, il a fallu interroger le sens et les actions possibles en lien avec les principes d'équité- diversité-inclusion (ÉDI) et les approches de décolonisation-réconciliation-autochtonisation (DRA). Ainsi, lors de la rédaction d'un plan d'action, la conception de programmes éducatifs ou encore le développement de projets dans le secteur de la santé, de nombreuses questions ont été soulevées. Nous proposons de partager notre réflexion qui sous-tend la distinction entre ces principes et ces approches.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".