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Record W2463059591 · doi:10.7202/1028188ar

Classification et analyse de collections d’objets de jeu selon le système ESAR : rapport de recherche

2015· article· fr· W2463059591 on OpenAlex
Manon Doucet, Rolande Filion, Denise Garon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueDocumentation et bibliothèques · 2015
Typearticle
Languagefr
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsCegep de Sainte FoyUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.208
GPT teacher head0.444
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it