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Record W3030497634 · doi:10.1093/deafed/enaa010

What’s That You Say? Communication Breakdowns and Their Repairs in Children Who Are Deaf or Hard of Hearing

2020· article· en· W3030497634 on OpenAlexaff
Erin T. Fitzpatrick, Bonita Squires, Elizabeth Kay‐Raining Bird

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2020
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsDalhousie University
Fundersnot available
KeywordsConversationFluencyPsychologyAudiologyHearing lossLanguage developmentDevelopmental psychologyLinguisticsCommunicationMedicine

Abstract

fetched live from OpenAlex

Conversational fluency is important to form meaningful connections and relationships with the people around us but is understudied in children who are deaf or hard of hearing (D/HH). Communication breakdowns reduce conversational fluency. They occur when a speaker says something that interrupts the flow of conversation requiring a request for clarification or confirmation from their listener to repair the misunderstanding. Young children who are D/HH are at risk of more frequent communication breakdowns and fewer successful repairs than children with typical hearing (The missing link in language development of deaf and hard of hearing children: Pragmatic language development. Seminars in Speech and Language, 33 (04), 297-309). About 14 children who were D/HH aged 7-12 year and 15 children with typical hearing were matched on chronological age. Comparisons of the number and duration of communication breakdowns, requests for repair, and responses to requests used by children in a 10-min conversation with an adult were completed. Results showed that while children who were D/HH demonstrated some differences, they were more similar to their typically hearing peers in communication breakdowns and repairs than previously reported in the literature.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.330

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 head0.092
GPT teacher head0.353
Teacher spread0.262 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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