Relationships matter: Supporting Aboriginal graduate students in British Columbia, Canada
Bibliographic record
Abstract
The current Canadian landscape of graduate education has pockets of presence of Indigenous faculty, students, and staff. The reality is that all too often, Aboriginal graduate students are either among the few, or is the sole Aboriginal person in an entire faculty. They usually do not have mentorship or guidance from an Indigenous faculty member orally, that is, someone who is supportive of Indigenous knowledges and Indigenity. While many institutions are working to recruit and retain Aboriginal graduate students, more attention needs to be paid to culturally relevant strategies, policies, and approaches. This paper critically examines the role of a culturally relevant peer and faculty mentoring initiative—SAGE (Supporting Aboriginal Graduate Enhancement)—which works to better guide institutional change for Indigenous graduate student success. The key findings show that the relationships in SAGE create a sense of belonging and networking opportunities, and it also fosters self-accountability to academic studies for many students because they no longer feel alone in their graduate journey. The paper concludes with a discussion on the implications of a culturally relevant peer-support program for mentoring, recruiting, and retaining Aboriginal graduate students. It also puts forth a challenge to institutions to better support Aboriginal graduate student recruitment and retention through their policies, programs, and services within the institution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".