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Influence des traitements graphomoteurs sur la production orthographique de mots isolés chez des élèves francophones du primaire

2019· dissertation· W7149530040 on OpenAlexaboutno aff
Érika Simard-Dupuis

Bibliographic record

Venuenot available
Typedissertation
Language
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionEscalation of commitmentContext (archaeology)Knowledge production

Abstract

fetched live from OpenAlex

Cette recherche doctorale, inscrite dans le champ de la psychologie cognitive, avait pour objectif d’évaluer l’influence des traitements graphomoteurs sur les traitements orthographiques au cours de la production écrite de mots isolés chez des élèves francophones du primaire. Trois études ont été menées dans le cadre de la théorie capacitaire (McCutchen, 1996), tant en France qu’au Québec, afin de confirmer et de préciser la nature de cet effet bottom-up de la graphomotricité sur l’orthographe lexicale.Dans l’ensemble, les résultats montrent qu’une augmentation du coût cognitif des traitements graphomoteurs capte les ressources cognitives disponibles en mémoire de travail pour le maintien et le rafraîchissement des représentations orthographiques maintenues temporairement actives dans le buffer graphémique, où elles subissent un déclin temporel.À notre connaissance, cette recherche doctorale est la première à montrer que l’effet bottom-up de la graphomotricité sur la réussite orthographique se localise au niveau du buffer graphémique, une instance mémorielle située à l’interface entre les processus centraux et les processus périphériques de l’écriture. Sur le plan pratique, les résultats obtenus soulignent la nécessité des interventions éducatives précoces visant l’automatisation des programmes moteurs, afin de réduire le coût cognitif des traitements graphomoteurs au profit des traitements orthographiques.

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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.293
Teacher spread0.280 · 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 teacher head, not a consensus.

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
Published2019
Admission routes1
Has abstractyes

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