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Record W3033271462 · doi:10.22158/sll.v4n3p1

Bilinguals and Their Perceptions of Both Languages in Their Brains

2020· article· en· W3033271462 on OpenAlexaff
Julia Falla-Wood

Bibliographic record

VenueStudies in Linguistics and Literature · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsBurman University
Fundersnot available
KeywordsPerspective (graphical)Neuroscience of multilingualismFirst languagePerceptionPsychologyIntersection (aeronautics)Representation (politics)Face (sociological concept)Second languageLinguisticsComputer scienceArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

This descriptive research examines the perception of bilingual students on the status of the mother tongue (L1) and the second language (L2) in their brains. The question of the influence or non-influence of L1 in using L2 has been studied under different theoretical frameworks. The issue of the representation of the languages in the brain has also been considered from a neurological perspective. However, no study has been undertaken on how bilinguals themselves perceive both languages in their minds. Do students see L1 and L2 as being together in one system, separate and independent of each other, or independent but sharing an intersection? The sample available to the researcher was 54 high school bilingual students. The research instruments are a questionnaire and a semi-structured face-to-face interview. The results of this study show that the highest percentage of students believe that both languages are independent of each other but share an intersection. All students have compared both languages, and have established differences and similarities between L1 and L2 through mental translations.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.043
GPT teacher head0.316
Teacher spread0.272 · 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 designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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