Évaluation des mémoires de master MEEF : quels aspects privilégiés par les enseignantes et les enseignants ?
Bibliographic record
Abstract
Les différentes réformes de la formation du personnel enseignant en France ont conduit à l’introduction d’un mémoire, professionnel d’abord, de recherche à visée professionnelle ensuite. Censé contribuer à former les étudiantes et étudiants à et par la recherche, cet écrit universitaire reste pourtant insuffisamment défini, ce qui pose la question des attentes des enseignants qui l’encadrent et l’évaluent. C’est à cette problématique que nous nous intéressons dans cet article en analysant les appréciations que les enseignants, issus de plusieurs disciplines, formulent sur les mémoires des étudiants de master 2 MEEF qui se préparent à enseigner à l’école primaire. Précisément, nous cherchons à déterminer les aspects privilégiés par les enseignants dans l’évaluation des mémoires.
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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 | 0.057 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".