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Record W3200841466 · doi:10.1016/j.bandl.2021.105014

Research on bilingualism as discovery science

2021· article· en· W3200841466 on OpenAlex
Christian A. Navarro‐Torres, Anne L. Beatty‐Martínez, Judith F. Kroll, David W. Green

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBrain and Language · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on AgingNational Institutes of HealthNational Science Foundation
KeywordsNeuroscience of multilingualismPsychologyCognitive scienceLinguisticsCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

An important aim of research on bilingualism is to understand how the brain adapts to the demands of using more than one language.In this paper, we argue that pursuing such an aim entails valuing our research as a discovery process that acts on variety.Prescriptions about sample size and methodology, rightly aimed at establishing a sound basis for generalization, should be understood as being in the service of science as a discovery process. We propose and illustrate by drawing from previous and contemporary examples within brain and cognitive sciences, that this necessitates exploring the neural bases of bilingual phenotypes:the adaptive variety induced through the interplay of biology and culture. We identify the conceptual and methodological prerequisites for such exploration and briefly allude to the publication practices that afford it as a community practice and to the risk of allowing methodological prescriptions, rather than discovery, to dominate the research endeavor.

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.

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)0.005
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 score0.018
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.417
Teacher spread0.348 · 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