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Record W4414696016 · doi:10.26443/mje/rsem.v59i2.10147

Amélioration de deux dispositifs de formation-accompagnement à l’implantation de programmes pour les élèves ayant une difficulté spécifique de la lecture-écriture

2025· article· fr· W4414696016 on OpenAlexaffvenue
Myriam Fontaine, Gabrielle Livernoche, André C. Moreau, Élisabeth Boily

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsUniversité du Québec à ChicoutimiUniversité du Québec en OutaouaisUniversité du Québec à Montréal
Fundersnot available
KeywordsScope (computer science)Production system (computer science)Rural developmentLimiting

Abstract

fetched live from OpenAlex

La présente recherche exploratoire présente les résultats de la mise à l’essai de deux dispositifs de formation-accompagnement à l’implantation de programmes de rééducation de l’identification et de la production de mots écrits d’élèves ayant une difficulté spécifique de la lecture-écriture1. Un dispositif général vise à implanter diverses pratiques basées sur des résultats probants, dont des programmes validés, et un dispositif spécifique vise l’implantation d’un programme intégrant des aides technologiques. Les objectifs de cet article permettent de décrire ce que les orthopédagogues participantes rapportent de leur expérience et observent comme effets sur l’apprentissage des élèves et sur leur développement professionnel. Des méthodes qualitatives de collecte et d’analyse de données sont utilisées. Les conditions d’amélioration des deux dispositifs sont dégagées.

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)0.009
metaresearch head score (Gemma)0.026
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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.002

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 head0.134
GPT teacher head0.428
Teacher spread0.294 · 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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueMcGill Journal of Education / Revue des sciences de l éducation de McGillSame topicFrench Language Learning MethodsFrench-language works237,207