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Record W2944450435 · doi:10.5539/ijel.v9n3p279

Does English Help You Accomplish More? Exploring the Instrumentality of English Learning in East and Southeast Asia

2019· article· en· W2944450435 on OpenAlexvenueno aff
Huiyu Zhang

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployee Welfare and Language Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsChinaSoutheast asiaSample (material)East AsiaGlobalizationPsychologyDemographic economicsPolitical scienceSociologyEconomicsLawEthnology

Abstract

fetched live from OpenAlex

This study empirically investigates the relationship between English proficiency and personal accomplishment in East and Southeast Asia. With the database of AsiaBarometer Survey 2006 and 2007, 15082 questionnaire respondents from China, Hong Kong of China, Japan, Korea, Singapore, Taiwan of China, Vietnam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines and Thailand formed the sample. We present the following findings with correlation and regression analysis: a) English proficiency positively influences personal accomplishment; b) the focal relationship is partly mediated by income, career and quality of life; and c) the focal relationship is positively moderated by international involvement. Such findings disclose and confirm the instrumentality of English learning in globalization. Theoretical and practical implications of the results are discussed.

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)0.005
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.244
Teacher spread0.225 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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