Transcender la diversité :: recourir à la théorie critique de la race et à la pensée féministe noire pour favoriser l’intégration des Noirs dans les admissions universitaires de premier cycle
Bibliographic record
Abstract
This paper argues that concrete actions are needed to address anti-Black racism and foster Black inclusion in Canadian higher education. These pertinent actions should target the systemic barriers faced by Black students when accessing post-secondary institutions. Through our reflections on current admissions practices of research-intensive Ontario-based universities, this paper highlights how currently used frameworks of diversity and inclusion may not be effective in disrupting the myth of meritocracy and mitigating systemic barriers faced by Black undergraduate applicants. We recommend that undergraduate admissions practices be grounded in a critical understanding of the four principles of the Scarborough Charter (Black flourishing, inclusive excellence, mutuality, and accountability) to support Black admissions, and that critical race theory and Black feminist thought be used as frameworks to create specific admissions practices and programs that disrupt anti-Black racism. The paper will generate further discussions on what it means to foster Black inclusion through university admissions and enrolment in a transformative manner.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.059 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 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".