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Record W4395465382 · doi:10.1037/xge0001564

Language diversity across home and work contexts differentially impacts age- and menopause-related declines in cognitive control in healthy females.

2024· article· en· W4395465382 on OpenAlexafffund
Alicia Duval, Anne L. Beatty‐Martínez, Stamatoula Pasvanis, Arielle Crestol, Jamie Snytte, M. Natasha Rajah, Debra Titone

Bibliographic record

VenueJournal of Experimental Psychology General · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsDouglas Mental Health University InstituteMcGill University
FundersNational Institute on AgingSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of HealthConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsPsychologyCognitionMenopauseDevelopmental psychologyDiversity (politics)Cognitive declineControl (management)GerontologyMedicineSociologyNeuroscience

Abstract

fetched live from OpenAlex

= 59) declines in cognitive control (as assessed by the Wisconsin Card Sorting Test) and to determine whether they are modulated by different facets of bilingual language experience, including the diversity of language use (i.e., language entropy) in home and workplace environments. Workplace but not home language diversity modulated age- and menopause-related declines in cognitive control, suggesting that females may compensate for decline by virtue of adapting to the externally imposed demands of the language environment. These findings have implications for identifying which aspects of bilingual experience may contribute to cognitive reserve in healthy aging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.554

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)0.0000.000

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 head0.014
GPT teacher head0.356
Teacher spread0.342 · 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 designObservational
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

Citations2
Published2024
Admission routes2
Has abstractyes

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