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Record W2160109160 · doi:10.1093/deafed/ens012

The Effect of Task in Deaf Readers' Graphophonological Processes: A Longitudinal Study

2012· article· en· W2160109160 on OpenAlexafffund
Daniel Daigle, Rachel Berthiaume, Élisabeth Demont

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2012
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPhonologyReading (process)Longitudinal studyCognitionTask (project management)Developmental psychologyPhonological awarenessAudiologyCognitive psychologyLinguistics

Abstract

fetched live from OpenAlex

This article reports on an investigation of graphophonological processes in deaf readers of French over a 1-year period. Deaf readers are known to have a phonological deficit compared to hearing peers, and conclusions from studies on this question are often conflicting. Among the different types of phonological processing, we can identify graphophonological processes based on correspondences between the oral and the written language. In this investigation, we evaluated graphophonemic and graphosyllabic processes using, in each case, two different tasks varying in their degree of cognitive constraints (CC- vs. CC+). Nineteen 11 year-old deaf students were compared to younger normal readers of the same reading level (RA, n = 17) and to normal readers of the same age (CA, n = 20). Two variables were considered in the analyses: accuracy and response latency. Results show that deaf readers do process written items at the graphophonological level and that graphophonological processes are related to reading ability. Also, results indicate main effects of task (CC- vs. CC+), time (T1 vs. T2), and group. In general, deaf participants' performances are comparable to those of RA and differ from those of CA. Results are discussed within the framework of the study of phonology in deaf readers and its relation to reading acquisition.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.379
Teacher spread0.344 · 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.

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

Citations8
Published2012
Admission routes2
Has abstractyes

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