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The Bilingual Brain: Brain processes during language acquisition

2020· article· en· W3093972686 on OpenAlexfundno aff
Rafaela Bepe Gabriotti, Rosângela Zomignan

Bibliographic record

VenueRevista Científica Multidisciplinar Núcleo do Conhecimento · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversity of WashingtonVrije Universiteit BrusselBộ Giáo dục và Ðào tạoUniversity of CambridgeMcGill University
KeywordsSecond-language acquisitionLanguage acquisitionNeuroscience of multilingualismDevelopmental linguisticsPortuguesePsychologyLinguisticsFirst languageComputer scienceComprehension approachLanguage educationMathematics educationNeuroscience

Abstract

fetched live from OpenAlex

This work is a study on language acquisition, brain processes involved during its acquisition and bilingualism. The aim of this research is to better understand how two languages are learning simultaneously, so that we can be better prepared to assist children during linguistic acquisition, as well as to support the teacher and family through theoretical foundation. Aspects such as the cortical organization of language, differences between the bilingual brain, compared to the monolingual brain, and influence of social interaction on linguistic learning are explained in this work to provide a broad view of bilingual language acquisition. For this study, we chose to use the bibliographic research of foreign literature, because not enough materials were found in the Portuguese that covered the areas of study contemplated. The results show how the brain processes language acquisition, shows the difference between learning two languages simultaneously, and sequentially, and presents how social factors and language are associated.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.313
Teacher spread0.295 · 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
Published2020
Admission routes1
Has abstractyes

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