Bibliographic record
Abstract
After a recent keynote on publishing the scholarship of teaching and learning (SoTL), a faculty member asked me the important question about the impact of these publications.As closely as I can remember, she said, "In medicine, we know that journal articles don't affect practitioner practice.How is SoTL any different?"Indeed, the medical education community has been raising this issue for some time.For instance, Richard Smith (2006) doesn't mince words: "Journals are not good at getting doctors to change and improve their practice.Words on paper rarely lead directly to change" (p.117).In her keynote at the 2008 conference of the International Society for the Scholarship of Teaching and Learning, Sue Clegg alluded to this research: what we know about professional learning from the communities of practice and informal learning literature suggests that the peer reviewed papers have very little, if any, impact on practice.Indeed the origins of systematic review in medicine were in recognition of precisely this problem-we know that Doctors and school teachers don't read this stuff.
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 | 0.027 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".