Classification et analyse de collections d’objets de jeu selon le système ESAR : rapport de recherche
Bibliographic record
Abstract
La classification et l’analyse des objets de jeu selon le système ESAR est un système inédit à six facettes. Ces facettes traduisent dans un tableau synthèse les étapes du développement de l’enfant à travers les principales formes d’activités ludiques et les grandes dimensions comportementales, tant aux points de vue cognitif, instrumental, social et langagier qu’affectif. Pour s’assurer d’une constance entre les éventuels utilisateurs de ce système, ce modèle d’analyse a été validé en partie par la méthode inter-juge. Toutes ces facettes, leur contenu ainsi que la validation sont décrits dans le présent article. Ce cadre méthodique s’inspire de la psychologie et des sciences documentaires et permet le classement et l’analyse du matériel ludique en faisant ressortir les habiletés qui différencient chacun des jeux et en reconnaissant sur le plan psychologique les apports spécifiques des jeux analysés.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".