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Record W1534177261 · doi:10.3917/nras.049.0139

Un environnement Web bilingue pour l'alphabétisation des personnes sourdes 

2010· article· fr· W1534177261 on OpenAlexaffabout
Daniel Daigle, Anne-Marie Parisot, Suzanne Villeneuve

Bibliographic record

VenueLa nouvelle revue - Éducation et société inclusives · 2010
Typearticle
Languagefr
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsForestryGeography

Abstract

fetched live from OpenAlex

La majorité des personnes sourdes accusent un retard important en lecture et en écriture (Daigle, 1998 ; Marschark et Spencer, 2003). En général, au terme de leur scolarisation, elles ont des performances à l’écrit comparables à celles d’enfants entendants de 9-10 ans (Holt, 1994 ; LaSasso, 1999). Ce constat n’est ni spécifique à une langue, ni caractéristique de la situation d’un pays en particulier. C’est par souci d’offrir aux adultes sourds de meilleures chances de réussite et grâce aux travaux des vingt dernières années portant sur la description de la Langue des signes québécoise (LSQ) et l’apprentissage du français écrit que des chercheurs québécois ont conçu et mis à l’essai un environnement Web bilingue (LSQ-français) destiné principalement aux adultes sourds, mais qui s’est avéré aussi intéressant pour les élèves sourds et les intervenants du milieu de la surdité. Le but de cet article est de décrire le cadre ayant servi à la mise en place de cet environnement Web et la démarche d’évaluation de la pertinence de cet outil rendu disponible pour la formation autant en présentiel qu’à distance (Parisot et al., 2009).

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.350
Threshold uncertainty score0.695

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.006

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 head0.031
GPT teacher head0.342
Teacher spread0.312 · 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 designNot applicable
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
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueLa nouvelle revue - Éducation et société inclusivesSame topicDigital Accessibility for DisabilitiesFrench-language works237,207