Bibliographic record
Abstract
À l’automne 2020, dans les pages de cette revue, j’ai proposé une réflexion sur l’évaluation et les impacts de la COVID-19 sur cette tâche particulièrement sensible du travail des enseignant.e.s. J’y ai notamment soutenu l’importance de revisiter les principes évaluatifs mis de l’avant par le Renouveau pédagogique en considérant les opportunités auxquelles ces derniers pavent la voie. Afin de rendre le tout un peu plus concret, j’ai tenté d’offrir, dans un webinaire organisé par le Conseil pédagogique interdisciplinaire du Québec (CPIQ) au mois d’avril 2021, des stratégies pour permettre aux enseignant.e.s de modifier leurs pratiques d’évaluation à l’échelle de la classe. D’autres que moi ont aussi proposé des réflexions allant en ce sens (voir par exemple : Conseil supérieur de l’éducation [CSE], 2018 ; Pasquini, 2021 ; Talbot & Gremion, 2022).
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 | 0.290 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.039 | 0.026 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".