Place-Based Indigenous Student Mentorship: Storying the Process of the Atlantic Indigenous Mentorship Network
Bibliographic record
Abstract
Introduction. Indigenous post-secondary education students bring unique insights, knowledges, and expertise into post-secondary education spaces. Yet, post-secondary education in Canada is often a site of marginalization for Indigenous students given its’ colonial underpinnings. Targeted mentorship initiatives can be a way to support Indigenous learners through their post-secondary education journeys. Purpose. This manuscript chronicles the creation and ongoing work of the Atlantic Indigenous Mentorship Network and shares insights, challenges, and goals for the network as it continues to support Indigenous students in the Atlantic region. Implications. Our network utilizes a multi-pronged mentorship approach through offering financial, learning, and mentorship opportunities guided by the voices of Indigenous students in the region. Notably, our ability to build relationships with and amongst Indigenous students is strengthened through having an Indigenous graduate student coordinating the network over more than four years. Given the diversity of Indigenous Peoples in the Atlantic, our network has and continues to capture and engage with diverse Indigenous learners in a way that pays respect to their unique knowledges and challenges through continuous consultation and engagement. Our success is demonstrated through the increase in funded students conducting Indigenous health research in the region and through feedback gathered directly from students engaged with the network. Conclusion. By sharing the story of the Atlantic Indigenous Mentorship Network, we hope that learnings and insight can be gleamed to create and continue to strengthen other mentorship initiatives directed towards supporting Indigenous students.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".