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Record W2536866420 · doi:10.1075/eurosla.16.01sab

Language processing in bilinguals

2016· article· en· W2536866420 on OpenAlex

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

VenueEUROSLA Yearbook · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNeuroscience of multilingualismLexiconMental lexiconCognitionPsychologyAge of AcquisitionCognitive psychologyLinguisticsNaturalismSet (abstract data type)Control (management)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we integrate and reinterpret new data from a set of experiments in our lab in order to tease apart many of the factors thought to influence bilingual processing. Specifically we combine data from studies investigating age of immersion (AoI), manner of acquisition (MoA), proficiency and context of bilingualism to (1) investigate the organization of the bilingual mental lexicon and (2) determine the nature of the interaction between bilingualism and cognitive control. We suggest that a naturalistic MoA promotes the integration of the bilingual lexicon, and that an early AoI per se is somewhat less important (though it tends to lead to a more naturalistic MoA). Further, bilinguals with an integrated bilingual lexicon (i.e., naturalistic learners) only develop cognitive control advantages if they are in a dual-language environment.

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)0.000
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 score0.038
Threshold uncertainty score0.570

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 head0.027
GPT teacher head0.297
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