A collective education mentorship model (CEMM): Responding to the TRC calls to action in undergraduate Indigenous health teaching
Bibliographic record
Abstract
In this paper, a Collective Education Mentorship Model (CEMM) is described by four non-Indigenous students who co-created and undertook a program with this model for an undergraduate-level university experiential learning experience centred around Indigenous health. This model is framed around shared teaching of students by various collaborators/mentors and built upon the values of collaboration, mentorship, reciprocity, and capacity building. Based on feedback from the students and collaborators involved in this experience, this model appears to be a promising means of better situating students as partners in experiential learning through the redefinition of student-supervisor roles, responsibilities, and the sharing of power. Furthermore, this model appeared to create more diverse experiences for students and minimized supervisor burden. Although this model was created specifically for the education of trainees in Indigenous health, it can be further adapted for other student placements and programs where these assets would be beneficial.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".