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Record W4243166133 · doi:10.31234/osf.io/s32zb

The Perceptions of Bilingualism Scales

2019· preprint· en· W4243166133 on OpenAlexaff
Gigi Luk, Sarah Surrain

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsMcGill University
FundersHarvard Graduate School of Education
KeywordsNeuroscience of multilingualismPsychologyPerceptionScale (ratio)Construct (python library)Variation (astronomy)Developmental psychologyReliability (semiconductor)Social psychologyCognitive psychologyGeographyComputer science

Abstract

fetched live from OpenAlex

An increasing number of children in the U.S. and around the world are exposed to multiple languages, yet there is considerable variation in individual bilingual outcomes. Previous research has shown that factors such as input, usage, and language history can help explain this variation, but less is known about the role of attitudes towards bilingualism, and no instrument currently exists for measuring such attitudes. Guided by previous theories on language perceptions, we describe two new scales developed to measure general perceptions of the value of bilingualism (study 1: Perceptions of Bilingualism, PoB) and parental perceptions of the value of bilingualism for one’s child (study 2: Perceptions of Bilingualism for child, PoB+) in the United States. We use factor analysis and Item Response Theory (IRT) to test the reliability, dimensionality, and individual item contributions of each scale using a national online sample of 422 adults (study 1) and a subsample of 321 parents (study 2). The final 10 and 8-item scales demonstrate internal reliability, unidimensionality, and precision of the intended construct to be measured. We report associations between scale scores and demographic characteristics and discuss how an IRT approach can complement classical approaches to attitude scale development. The PoB and PoB+ are useful tools to explore the association between social perceptions of bilingualism and language use for oneself and for one’s child.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score1.000

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)0.0010.000

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 head0.087
GPT teacher head0.506
Teacher spread0.420 · 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.

Study designNot applicable
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

Citations10
Published2019
Admission routes1
Has abstractyes

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