SPECIAL FEATURE /CONTRIBUTION SPÉCIALE Teaching Excellence: A Reaction to
Bibliographic record
Abstract
This paper has been written partially in response to the Smith Commission Report, and partially in response to the reactions the report has elicited already. The Smith Commission Report voiced many valid concerns about teaching excellence; however, many of the so-called "innovations " that have been developed in answer to Stuart Smith's call for teaching excellence are, in fact, little different from those techniques implemented under the auspices of the Ontario Universities Program for Institutional Development (OUPID) in the 1960's and early 1970's. This being the case, the authors feel that the most likely result will be a similar lack of success. It is, therefore, our suggestion that an attempt ought to be made to change the infrastructure of the university sys-tem so that it supports good teaching and research with equal measure. This, above all else, should lead to real improvements in the quality of teaching.
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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.054 | 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".