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Record W2583322683 · doi:10.1017/s136672891500067x

The time course of cross-language activation in deaf ASL–English bilinguals

2015· article· en· W2583322683 on OpenAlexaff
Jill P. Morford, Corrine Occhino, Pilar Piñar, Erin Wilkinson, Judith F. Kroll

Bibliographic record

VenueBilingualism Language and Cognition · 2015
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Manitoba
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsLinguisticsNeuroscience of multilingualismPsychologyAmerican Sign LanguageSign languageSecond languageTask (project management)

Abstract

fetched live from OpenAlex

What is the time course of cross-language activation in deaf sign-print bilinguals? Prior studies demonstrating cross-language activation in deaf bilinguals used paradigms that would allow strategic or conscious translation. This study investigates whether cross-language activation can be eliminated by reducing the time available for lexical processing. Deaf ASL-English bilinguals and hearing English monolinguals viewed pairs of English words and judged their semantic similarity. Half of the stimuli had phonologically related translations in ASL, but participants saw only English words. We replicated prior findings of cross-language activation despite the introduction of a much faster rate of presentation. Further, the deaf bilinguals were as fast or faster than hearing monolinguals despite the fact that the task was in their second language. The results allow us to rule out the possibility that deaf ASL-English bilinguals only activate ASL phonological forms when given ample time for strategic or conscious translation across their two languages.

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 score0.302
Threshold uncertainty score0.410

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 head0.032
GPT teacher head0.379
Teacher spread0.347 · 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

Citations32
Published2015
Admission routes1
Has abstractyes

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