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Record W4361274099 · doi:10.1017/s1366728923000202

Does Language Entropy Shape Cognitive Performance? A Tale of Two Cities

2023· article· en· W4361274099 on OpenAlexafffundabout
Danika Wagner, Katerina Bekas, Ellen Bialystok

Bibliographic record

VenueBilingualism Language and Cognition · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEntropy (arrow of time)CognitionCognitive psychologyPsychologyLanguage proficiencyNeuroscience of multilingualismLinguisticsComputer scienceNatural language processingMathematics education

Abstract

fetched live from OpenAlex

Abstract Research examining the cognitive consequences of bilingualism has increasingly relied on continuous measures to capture the degree and nature of bilingual experience, using such variables as proficiency, age of acquisition, and language environments. One such measure, language entropy, indexes the social diversity of contexts in which each language is used. The construct was developed in a particular bilingual context, Montréal, Canada. The present study investigated the extent to which it also applies to a context in which social language use is substantially different from that of Montréal – namely, Toronto, Canada. Following the procedures in the original study, participants were assigned an entropy score and performed the AX-Continuous Performance Task (AX-CPT). Performance was associated with self-rated language proficiency, but unlike the results from Montréal, was not associated with entropy scores. Therefore, differences in the language context influence whether language entropy is related to behavioral performance on a cognitive task.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.294
Teacher spread0.273 · 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 designBench or experimental
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

Citations21
Published2023
Admission routes3
Has abstractyes

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