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Record W3094387270 · doi:10.5539/jel.v9n6p1

Does Critical Period Affect Bilingual Advantage for Working Memory and Metacognition?

2020· article· en· W3094387270 on OpenAlexvenueno aff
Kexuan Huang

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMetacognitionNeuroscience of multilingualismAffect (linguistics)Reading comprehensionPeriod (music)Language acquisitionReading (process)Cognitive psychologyComprehensionWorking memoryDevelopmental psychologyCognitionLinguisticsMathematics educationCommunication

Abstract

fetched live from OpenAlex

There have been many studies exploring the advantages that bilingualism confers to individuals’ working memory and metacognition (see Ransdell, 2006; Del Missier et al., 2010). The hypothesis of language critical period states that if no language learning and teaching happen during the critical period, an individual will never be able to fully grasp any language to a full extent (Fromkin et al., 1974). This study investigates whether late bilingualism (second language acquisition after the critical period) will positively affect a person’s working memory and metacognition just like early bilingualism (second language acquisition before the critical period) does. Sixty Chinese persons between the ages of 18 and 35 participated in my online experimental protocol, including a language experience questionnaire, a reading comprehension exam, and a reading span task. I found that late bilingualism poses a similar advantage to an individual’s working memory as early bilingualism, while it negatively affects an individual’s metacognitive awareness of their own language ability.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.358
Teacher spread0.319 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and Learning→Same topicNeurobiology of Language and Bilingualism→French-language works237,207→