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Record W2606911034 · doi:10.1097/tld.0000000000000118

Learning to Read in English and French

2017· article· en· W2606911034 on OpenAlexaff
Sheila Cira Chung, Poh Wee Koh, S. Hélène Deacon, Xi Chen

Bibliographic record

VenueTopics in Language Disorders · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsSocial Sciences and Humanities Research CouncilInstitute for Christian StudiesUniversity of TorontoDalhousie University
Fundersnot available
KeywordsPsychologyPhonological awarenessReading (process)VocabularyLinguisticsWord recognitionPhonologyLearning to readLongitudinal studyVocabulary development

Abstract

fetched live from OpenAlex

This longitudinal study investigated the predictors of word reading in English and French for 69 children in early total French immersion from first through third grade. The influence of phonological awareness, orthographic processing, and vocabulary in English and French on the achievement and growth of word reading in the 2 languages were evaluated with growth curve analyses. Findings revealed that predictors in Grade 1 were similar for English and French word reading in Grade 3. Specifically, English phonological awareness and orthographic processing predicted English word reading achievement; English orthographic processing also predicted growth in English word reading. French phonological awareness and orthographic processing in Grade 1 predicted French word reading achievement in Grade 3. Furthermore, at-risk readers identified in Grade 1 (n = 6) generally fell behind their typically developing peers across all measures, although evidence of improvement emerged over time.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.318
Teacher spread0.308 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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