Reflections on pedagogical practice and development through multidisciplinary triadic peer mentorship
Bibliographic record
Abstract
This article presents a critical reflection on the experiences of three university instructors (two teaching stream and one tenure stream) within a 6-month peer-to-peer mentoring for teaching community of practice (P2P CoP). As part of the P2P CoP, the authors (who were previously unknown to one another) formed a “teaching triad” at a tri-campus, research-intensive Canadian university. They regularly met in person for 1 hour on a weekly basis throughout the Winter 2019 semester to discuss teaching-related matters, undertook classroom visits to observe one another teach, and participated in pedagogical workshops with other P2P CoP members. In this article, the authors specifically reflect on (a) the opportunities presented for reciprocity within their triadic mentorship structure; (b) the value their different scholarly fields offered them in pursuit of professional development and open exchange; and (c) the broadened knowledge base of pedagogical techniques their multidisciplinarity afforded them throughout the P2P CoP. They interpret their experiences of building relationships to offer insights into the unique and transformative advantages of teaching triad mentorship models. These include faculty peer mentoring and professional development opportunities that are not merely formalized but institutionally supported and related benefits for other institutions of higher education.
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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 | 0.038 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.035 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.015 |
| 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".