Mentor and the ‘Tea and Cookies’ Mentorship Approach: A Conversation With Ian Winchester
Bibliographic record
Abstract
Abstract: This article seeks to explore the complex relationship between mentors and mentees and how it may impact the development of junior researchers’ potential. The study applies an ethnography approach to explore the perceptions of experiences of Ian Winchester, a scholar with over 50 years of research and teaching who has mentored hundreds of doctoral and master’s level graduate students at two large Canadian universities, the University of Toronto and the University of Calgary. It is an ethnographic conversation between the mentor and a mentee on the journey of mentorship in academia. The results are drawn from Winchester’s answers to ten semi-structured questions guiding many novice researchers on the path to humanities and social sciences scholarship. This ethnographic conversation may contribute to a better understanding of the challenges facing current mentorship practices in academia. It may also partly serve as a guide to those just entering the practice, whether as mentors or mentees.
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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 | 0.043 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.035 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".