Discriminations au sein des professions et métiers documentaires au Québec, qu'en est-il? Résultats d'une enquête sur la réconciliation, l'équité, la diversité et l'inclusion (RÉDI)
Bibliographic record
Abstract
Cet article examine les enjeux de réconciliation, d’équité, de diversité et d’inclusion (RÉDI) dans les milieux documentaires québécois, à travers une enquête réalisée par la Fédération des Milieux Documentaires (FMD) et l’Université de Montréal. Fondée sur un questionnaire en ligne complété par 602 personnes participantes, l’étude dresse un portrait inédit et détaillé des travailleurs et travailleuses, de leurs milieux de travail et des discriminations vécues ou observées. Les résultats révèlent une diversité présente, mais également des discriminations persistantes, des tensions interculturelles et des défis institutionnels liés à l’identité, l’ethnicité, le genre et les handicaps, malgré les efforts déclarés. Discriminations in the Informational Science Field in Quebec, What’s the Situation? Results of a Study on Reconciliation, Equity, Diversity and Inclusion (REDI) AbstractThis article examines the issues of reconciliation, equity, diversity and inclusion (REDI) in the informational science field in Quebec, through a study conducted by the Fédération des Milieux Documentaires (FMD) and the Université de Montréal. Based on an online survey completed by 602 participants, the study paints an unprecedented and detailed picture of the workers, their workplace, and the discrimination experienced or observed. The results reveal a diversity in the participants, but also discrimination, intercultural tensions and institutional challenges linked to identity, ethnicity, gender and handicaps, despite reported efforts. KeywordsInformational science field in Quebec; Informational science occupations and professions; Equity; Diversity; Inclusion; Discriminations
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".