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Record W2802808546 · doi:10.14328/mes.2015.9.30.197

Current status and future directions of language education policy for linguistic minority students

2015· article· en· W2802808546 on OpenAlexaboutno aff
Kyung-Hwan Mo, Jae-Boon Lee, Jong Myung Hong, Jeong Soo Lim

Bibliographic record

VenueMulticultural Education Studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsCurrent (fluid)Political scienceMinority languageSociologyPsychologyPhysics

Abstract

fetched live from OpenAlex

The purpose of this study is to address the current status and future directions of language education policies for linguistic minority students. Based on the analysis of research on language development of diverse students, language education policies of multicultural societies such as Germany, Canada, the United States, and Japan, and the current KSL and bilingual education in Korea, this article suggests improvement plans for multicultural language education. First of all, language education for students from diverse families should contribute to the development of academic achievement and bilingual literacy, educational equality, and global citizenship required to function effectively in this globalized world. Standard curriculum and teaching materials should be developed. It is also required to develop effective instruction models and share exemplary practices. All students in need of KSL and bilingual education have the right to be taught by high-quality teachers. The Increasing number of the students whose first languages are not Korean introduces more accountability for teachers, schools, and education offices.

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)0.017
metaresearch head score (Gemma)0.018
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.005
Scholarly communication0.0100.008
Open science0.0030.006
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.059
GPT teacher head0.405
Teacher spread0.346 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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