Reciprocal Mentorship as Trans-Systemic Knowledge: A Story of an Indigenous Student and a Non-Indigenous Academic Supervisor Navigating Graduate Research in a Canadian University
Bibliographic record
Abstract
Reciprocal mentorship is how Indigenous students and non-Indigenous supervisors can supportively navigate their way through graduate research in higher education. Reciprocal mentorship as trans-systemic knowledge values both Indigenous and Eurocentric worldviews, whereby the student has the expertise from Indigenous community and the academic supervisor has the expertise in the academic world. Through sharing stories of their research journey within a Canadian University, Webster and Bishop offer key insights around engaging in reciprocal mentorship, navigating the two-worlds, finding a common language, and having shared values. As a result, Indigenous and non-Indigenous students and supervisors may see themselves within the stories and seek reciprocal mentorship to be successful in the academic research and educational journey and make an impact in their university and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.770 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.399 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.727 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".