Bibliographic record
Abstract
In this chapter, the authors share their reflections on the practice of using a community-based approach to doing SoTL research. They examine two professional development programs at their respective institutions—York University and Humber College in Ontario, Canada—that support faculty members' engagement in SoTL research. EduCATE and the Teaching Innovation Fund are two variations of SoTL programs in which participants come together to engage in and support each other through the process of doing SoTL research and are organized around participants' individual goals rather than a predetermined set of outcomes. The authors provide a fulsome narrative and reflective account of the EduCATE and Teaching Innovation Fund programs with a particular focus on each program's development and relative success. Throughout, the impact of SoTL as a form of professional development is emphasized.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".