Bibliographic record
Abstract
vant d'entrer dans le vif de notre sujet, nous aimerions apporter quelques précisions. L'ÉVALUATION DES APPRENTISSAGESÉvaluer, lorsqu'il est question d'évaluation des apprentissages, c'est, selon nous, diffuser une interprétation d'informations sur l'atteinte d'objectifs préalablement établis, afin de permettre aux personnes concernées de prendre des décisions pertinentes.Le geste d'évaluer revêt donc plusieurs aspects : élaborer des moyens d'obtenir des données qualitatives ou quantitatives en ce qui touche l'atteinte des objectifs, faire en sorte que les résultats soient liés au travail d'apprentissage, interpréter les résultats en tenant compte des circonstances de l'évaluation, diffuser les résultats aux personnes concernées, générer de l'information pertinente tant pour l'évaluateur que pour l'évalué, permettre de prendre des décisions au sujet de la poursuite ou de la sanction de l'apprentissage.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".