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Record W2166767092 · doi:10.1093/deafed/enq020

How Do Deaf Signers of LSQ and Their Teachers Construct the Meaning of a Written Text?

2010· article· en· W2166767092 on OpenAlexaff
Daphné Ducharme, Isabelle Arcand

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2010
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMeaning (existential)PsychologyConstruct (python library)Reading (process)Exploratory researchRecallLinguisticsMathematics educationMeaning-makingCognitive psychologySociologyComputer science

Abstract

fetched live from OpenAlex

Many studies have investigated why learning to read is so problematic for deaf individuals. However, we still know very little about how to teach reading to signing students. In this article, we report on an exploratory qualitative study of deaf LSQ (Langue des signes québécoise) signers learning to read with two teachers, in an effort to better understand what strategies might be most useful in constructing the meaning of a text. By videotaping reading sessions between each teacher and student, then conducting recall interviews, we found that both students and teachers used a number of strategies to construct meaning. The list of strategies observed was categorized as word attack or global meaning types. Developing readers showed different patterns of strategy use, with more global meaning strategies being used by the more independent reader. We also found that the deaf teacher and hearing teacher had different patterns of strategy use, although both favored global meaning types. Finally, our findings indicate that both teachers adapted their strategy use to the needs of the students, but with a different focus. Namely, the deaf teacher used more global meaning strategies with the weaker reader and less with the more independent reader, whereas the hearing teacher showed the opposite pattern.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.332
Teacher spread0.301 · 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 designQualitative
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

Citations5
Published2010
Admission routes1
Has abstractyes

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