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Record W2889519118 · doi:10.1017/s1366728918000950

Who is bilingual? Snapshots across the lifespan

2018· article· en· W2889519118 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBilingualism Language and Cognition · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsYork University
Fundersnot available
KeywordsNeuroscience of multilingualismPsychologyVariance (accounting)Developmental psychologyFactorial analysisLanguage proficiencyYoung adultMathematics educationMathematicsStatistics

Abstract

fetched live from OpenAlex

Building on our earlier analysis of the factorial structure of bilingualism for young adults obtained from the Language and Social Background Questionnaire (LSBQ; Anderson, Mak, Keyvani Chahi & Bialystok, 2018), we analyzed responses from 675 children and 125 older adults to a similar questionnaire. Three factors accounting for 74% of the variance emerged in the analysis of children's responses: Adult Language in the Home, Non-English use for Media, Non-English use with Siblings. There were also three factors that explained the responses of older adults that accounted for 79% of the variance: Non-English Use, Non-English Proficiency, and English Proficiency. Therefore, bilingual experience is captured by different factors at different points in the lifespan. These results are discussed in conjunction with the earlier results from young adults and the implications for understanding bilingualism across the lifespan.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.020
GPT teacher head0.344
Teacher spread0.324 · 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