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Record W4411573204 · doi:10.1017/s1366728925100229

Contextual diversity and picture naming: The role of aging and bilingualism

2025· article· en· W4411573204 on OpenAlexafffund
Mikayla Trudeau-Meisner, Brendan T. Johns, Vanessa Taler

Bibliographic record

VenueBilingualism Language and Cognition · 2025
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsMcGill UniversityBruyèreUniversity of Ottawa
FundersAlzheimer Society
KeywordsNeuroscience of multilingualismPsychologyDiversity (politics)LinguisticsCognitive psychologySociologyNeuroscienceAnthropology

Abstract

fetched live from OpenAlex

Abstract Word frequency has long been considered an essential aspect of psycholinguistic theory. However, research has shown that measures of contextual and semantic diversity provide a better fit to lexical decision and naming data than word frequency. The current study examines the role of contextual and semantic diversity in picture naming ability across aging and bilingualism. A picture naming experiment was conducted with six groups of participants: younger monolinguals, older monolinguals, younger L1 English bilinguals, older L1 English bilinguals, younger L2 English bilinguals and older L2 English bilinguals. Consistent with previous findings, the contextual diversity measure accounted for more variance in the picture naming data than word frequency. Furthermore, older adults and L1 English bilinguals were more sensitive to semantic diversity information, while younger adults and L2 English bilinguals relied more on age of acquisition in their lexical organization.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.010
GPT teacher head0.280
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes2
Has abstractyes

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