“Keep It 100”: A Handbook Promoting Equitable Outcomes for Black University Students Through Mentorship
Bibliographic record
Abstract
Black and racialized students attend Canadian universities with the intent of achieving academic success. However, instances of overt and covert racism negatively impact Black and racialized students’ academic success and retention rates in university programs. Lee (1999) and Sinanan (2016) suggest mentorship as a key strategy towards increasing academic success and retention rates among Black students. This handbook proposes mentorship strategies for use by university educators and administrators to help build beneficial relationships with Black and racialized students that lead to improved learning outcomes. Specifically, this handbook proposes what Quach et al. (2020) have identified as mentee-focused mentorship. Mentee-focused mentorship centres on the needs of Black students and recognizes the layers of systemic racism that exist in universities. This project provides educators and administrators with an understanding of concepts related to systemic racism, anti-racism, intersectionality, critical race theory (CRT) and CRT-informed practices. Personal stories from Black students collected from the academic literature are presented alongside points of reflection for educators and administrators. Points of reflection are provided with the intent that readers will meaningfully consider their positions of power and the strengths in students’ non-academic identities.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".