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Record W4405324746 · doi:10.4000/12whu

Black students’ Mental Health Matters : une étude du racisme structurel académique sur les campus canadiens

2022· article· fr· W4405324746 on OpenAlexaffabout
Agnès Berthelot-Raffard

Bibliographic record

VenueArchipélies · 2022
Typearticle
Languagefr
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsYork University
Fundersnot available
KeywordsSociologyMental healthPolitical sciencePsychologyPsychiatry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.027
GPT teacher head0.361
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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