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Record W7067352193

Language and Literacy Achievement of Deaf Students in Minority French Settings

2025· other· en· W7067352193 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2025
Typeother
Languageen
FieldEnvironmental Science
TopicEcology, Conservation, and Geographical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroscience of multilingualismLiteracyContext (archaeology)Spoken languageLanguage proficiencyDeaf educationFirst languageFrench
DOInot available

Abstract

fetched live from OpenAlex

This doctoral study investigated the bilingual spoken language and literacy achievement of four francophone deaf learners living in an English-dominant area of Ontario in Canada. Much bilingual research in the field of Deaf Education prioritizes learner experiences in signed bilingual-bicultural settings and reports of a grade four literacy level continue to plague the field (Allen, 1986; Qi & Mitchell, 2012; Traxler, 2000). The current changed context reflecting the implementation of Universal Newborn Hearing Screening and improved technology indicates language and literacy achievements commensurate with typically-hearing-aged peers. Meanwhile, evidence of spoken language bilingualism among deaf learners is limited, despite the growing number of spoken language bilingual and multilingual deaf learners. In Canada, proficiency in both official languages (i.e., French and English) is an asset and a right for children of francophone parents (see Section 23 of the Canadian Charter of Rights); it is also well-known that bilinguals enjoy many lifelong cognitive and social benefits. There are an increasing number of deaf learners from bilingual/multilingual homes, however, topics related to spoken language bilingualism have only recently started to emerge in the literature. This mixed-methods case study gathered data from four deaf students with various experiences of enrolment in minority French-language schools to highlight their bilingual potential when given early access to technology and appropriate supports while living in an English dominant community. Parent participants (n = 8) and student participants (n = 4) engaged in semi-structured interviews to provide data towards developing a detailed profile of each learner (Seidman, 2006). Parent participants provided additional information by responding to a questionnaire eliciting early intervention experiences and home language and literacy practices. The scores on the CAP and the SIR checklists illuminate each learners’ auditory access in both languages. All four student participants engaged in four separate assessment sessions with two standardized measures (i.e., CELF-5 & WIAT) completed in French and English. Thematic analysis of the interviews alongside the extensive standardized assessment results revealed three major findings: 1) deaf learners have the potential to become balanced bilinguals in language and literacy; 2) in Ontario, there is limited support for spoken language bilingualism at identification; 3) early access to technology paired with early intervention alongside robust home literacy practices may contribute to strong bilingual spoken language and literacy development for deaf learners. The data revealed average to above average achievement on all four standardized assessments corroborating the conversational skills observed during the bilingual interviews and assessment sessions. Findings reveal that there is a need for more evidence-based research to identify the range of potential across the heterogeneous group of deaf learners across minority languages. Given the small participant size, this study serves as a launching point for future research into spoken language bilingualism among deaf learners especially in response to minority language learning or language maintenance.

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 score0.451
Threshold uncertainty score0.907

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.172
Teacher spread0.168 · 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 routes1
Has abstractyes

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