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Record W2913345549 · doi:10.1093/deafed/eny037

A Cross-cultural Mixed Methods Investigation of Language Socialization Practices

2018· article· en· W2913345549 on OpenAlexaffabout
Hillary Ganek, Stephanie Nixon, Alice Eriks‐Brophy

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsVietnameseSocializationPsychologyIdentity (music)Context (archaeology)Developmental psychologySocial psychologyLanguage acquisitionAppealAdaptation (eye)LinguisticsMathematics education

Abstract

fetched live from OpenAlex

This embedded mixed methods study explores how cultural differences in language socialization practices influence parent-child verbal interactions. The Language ENvironment Analysis (LENA) System audio recorded families of children who are and are not deaf and hard of hearing in Canada and Vietnam. Software automatically calculated an average conversational turn count. Canadian families participated in more turns than Vietnamese families regardless of hearing status. Interviews with the children's caregivers provided context for these results. Within Vietnamese families, the language socialization practice "Intelligence" results in reduced opportunities for turn-taking, while the Canadian focus on creating personal "Identity" encouraged them. "Intelligence" encompasses Vietnamese participants' desire to ensure their children are learning and "Identity" expresses the Canadian participants' appeal to encourage individuality in their children. The findings suggest directions for the adaptation of intervention. It is the first known study to incorporate LENA results into a mixed methods design.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.071
GPT teacher head0.511
Teacher spread0.441 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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