Bibliographic record
Abstract
La discipline de l’évaluation de programme a déployé plus d’énergie à développer les méthodologies au cours des vingt dernières années qu’à comprendre certains enjeux stratégiques, pour ne citer que le raisonnement évaluatif qui soutient la pratique. Certains auteurs, par exemple Fournier et Scriven, se sont plus particulièrement intéressés à l’influence du raisonnement évaluatif sur les conclusions ainsi que sur les justifications mais cela ne semble pas avoir suscité beaucoup d’intérêt jusqu’à ce jour. Ce constat est d’autant plus surprenant que le passage de la description au jugement constitue la pierre angulaire de toute évaluation de programme de qualité. C’est dans cette perspective que le présent article aborde la question du raisonnement sous-jacent à toute démarche évaluative. Plus précisément, l’auteure aborde la façon dont les évaluateurs produisent et justifient les jugements émis.
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 | 0.131 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".