Les paradoxes de l'évaluation de l'enseignement universitaire
Bibliographic record
Abstract
Cet article tente d'approcher la complexité des processus d'évaluation de l'enseignement tels qu'ils sont vécus et ressentis dans les universités québécoises. Par une méthodologie à la fois quantitative et qualitative sont ici dégagées les grandes tendances de réponses d'une population de chargés de cours et de professeurs au sujet des buts, des moyens, des sources et des modalités de l'évaluation de l'enseignement. Les sujets s'entendent, sans distinction majeure de statut, sur les moyens et les modalités d'évaluation. En revanche, le profil de l'évaluateur idéal semble difficile à cerner. Les autrices relèvent de nombreux paradoxes dans les réponses des sujets interrogés, reflet probable de la complexité d'une question qui reste sensible dans le monde universitaire.
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 | 0.126 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".