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Literature Review on Second Language Acquisition: Looking at the Impact of Bilingual Education at Different Times on Intercultural Competence

2024· article· en· W4404488730 on OpenAlexaff

Bibliographic record

VenueCommunications in Humanities Research · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntercultural competencePsychologyCompetence (human resources)LinguisticsSecond-language acquisitionPedagogySociologySocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

With the prevalence of globalization, more and more parents are caught in a state of anxiety — they want to give their children a bilingual education as early and as well as possible so that in the future they can send their children to study abroad. Hence comes the existence of bilingual schools. However, parents are increasingly choosing bilingual kindergartens because they believe they will enhance their children's linguistic and intercultural competence. This literature review aims to illustrate the distinct impacts of attending a bilingual kindergarten versus a bilingual high school on students' intercultural competence, enlightening readers about the distinctions between the two, and highlighting the importance of early enrollment in a bilingual education system. This article concludes that bilingual kindergartens could subtly let students learn the language and its culture, and bilingual high schools would focus more on the practical way of learning a language but less on the cultural aspects. They both have their advantages and promote intercultural competence in different ways.

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)0.002
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.017
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.109
GPT teacher head0.425
Teacher spread0.316 · 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
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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