The International Society for the Scholarship of Teaching and Learning
Bibliographic record
Abstract
Humanities Research Council of Canada and by les Fonds pour la formation des chercheurs et aide à la recherche du Québec. 2Inquiry into teaching and learning The invitation to participate in this panel stated that its purpose is to examine how the scholarship of teaching and learning is taking shape in different national contexts, sketching its history, place, and prospects in the political culture of higher education in our countries. We were asked to address the most salient or problematic issues that shape or affect the engagement of mainstream faculty members in and with scholarly work on teaching and learning in their classrooms and in their disciplines. The first issue I propose to consider, because it is fundamental to our discussion, is our conception of the scholarship of teaching and learning and what it requires of us. To further situate our discussion, I will then offer a perspective on the route taken in the development of research on postsecondary teaching and learning in North America and Europe. Next I will trace developments in Canada over the last four decades. Chief among these developments are the search for great exemplars of teaching, the findings of the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.032 |
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".