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Record W420525195 · doi:10.7202/1091270ar

Un goût de lire bien mesuré. Élaboration et mise à l’essai d’une échelle d’attitudes envers la lecture

2022· article· fr· W420525195 on OpenAlexvenueno aff
Dominique Lafontaine

Bibliographic record

VenueMesure et évaluation en éducation · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersFédération Wallonie-Bruxelles
KeywordsHumanitiesPsychologyPolitical scienceArt

Abstract

fetched live from OpenAlex

Le présent article expose les résultats d’une recherche consacrée à la mise au point et à la mise à l’épreuve (contrôle des qualités psychométriques) d’une échelle mesurant les attitudes envers la lecture-loisir, destinée à un public d’élèves de l’enseignement secondaire. Testée sur un échantillon de 2861 élèves de 14-15 ans en Communauté française de Belgique, l’échelle se comporte de façon fidèle et apparaît comme un instrument valide de mesure des attitudes envers la lecture, apparemment peu affecté par la désirabilité sociale et la tendance à acquiescer, du moins dans le contexte de la Communauté française de Belgique.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.030
GPT teacher head0.363
Teacher spread0.333 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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