Black students’ Mental Health Matters : une étude du racisme structurel académique sur les campus canadiens
Bibliographic record
Abstract
Bien que l’enseignement supérieur canadien fasse la promotion des valeurs d’équité, de diversité, d’inclusion (EDI) et de multiculturalisme, les personnes étudiantes issues de la communauté noire sont, sur leur campus, continuellement confrontés aux discriminations, microagressions, et aux autres multiples expressions du racisme anti-Noir telles que les injustices épistémiques. Les personnes étudiantes noires sont, en conséquence, plus à risque que leurs pairs d’être affectées par des problèmes de santé mentale auxquels s’ajoutent ceux liés au stress racial (race-related stressor) et à la charge mentale qu’il impose de facto. La première partie de cet article définira le racisme structurel académique et montrera sa spécificité en ce qui a trait à l’expérience les personnes étudiantes noires sur les campus canadiens. La seconde partie fera état des problèmes de santé mentale qui découlent de l’exposition au racisme structurel. Cet article démontre que la mise en œuvre du bien-être académique (academic well-being) est la clef de la lutte contre le racisme sur les campus et de la promotion des valeurs d’équité, de diversité et d’inclusion qui la sous-tend.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".