Indigenous mentor’s understandings of being a mentor in higher education: insights from a Canadian university
Bibliographic record
Abstract
This article introduces the voices of Indigenous mentors, which have been overlooked in mentoring research. This study addressed how mentors understood their role in nurturing student competence, connection, and agency; key ingredients of self-determination. Indigenous mentors participated in conversational interviews, which were examined from traditional academic and pastoral perspectives and from the perspective of self-determination theory. Six themes emerged from the analysis: mentors as knowledge brokers; facilitators of belongingness; supportive and empowering, guides, self-managers and as enablers to help mentees become self-determined. Most of these themes align with previous literature on mentoring and add insight into a small but growing body of research findings on student mentors from Indigenous backgrounds. Notably, one of the themes, mentors as self-managers, has largely been neglected in research on mentoring involving students from Indigenous backgrounds. The implications for giving voice to Indigenous mentor views are discussed in the concluding section.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".