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Record W2081956185 · doi:10.1017/s0142716414000198

Neuroplasticity as a model for bilingualism: Commentary on Baum and Titone

2014· article· en· W2081956185 on OpenAlexaff
Ellen Bialystok

Bibliographic record

VenueApplied Psycholinguistics · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsYork University
FundersNational Institutes of Health
KeywordsCognitive reservePsychologyNeuroscience of multilingualismCognitionSternNeuropathologyCognitive declineCognitive skillCognitive remediation therapyDevelopmental psychologyCognitive psychologyNeuroscienceDementiaCognitive impairmentMedicineDisease

Abstract

fetched live from OpenAlex

The inevitable decline of cognitive function with aging and the high incidence of clinical impairment make understanding the process of cognitive decline and the search for remediation an urgent priority. However, in spite of massive efforts in research and development, the effectiveness of pharmacological treatments for cognitive impairment remains extremely limited (Zhu et al., in press). Therefore, there is growing interest in the set of lifestyle factors that serve to maintain cognitive function even in the presence of neuropathology. These factors, called cognitive reserve (Stern, 2002), include education, occupational status, socioeconomic class, and involvement in physical, intellectual, and social activities (Bennett, Schneider, Tang, Arnold, & Wilson, 2006; Stern et al., 1994). Bilingualism appears to be another potent source of cognitive reserve (Bialystok, Craik, Green, & Gollan, 2009). For these reasons, a comprehensive review of the small but growing literature on bilingualism and cognition in aging is timely and scientifically important.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.035
GPT teacher head0.319
Teacher spread0.283 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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