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Record W2899769868

Becoming Multicultural in South Korea: Listening to the Voices of Multicultural Parents in Early Childhood Schools

2012· article· en· W2899769868 on OpenAlexvenueno aff
Yoon H. Lee, Yoo Seon Bang

Bibliographic record

VenueEarly childhood education · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPsychosocial Factors Impacting Youth
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismImmigrationMulticultural educationActive listeningTransformative learningDiversity (politics)PsychologyCultural diversityEarly childhood educationEarly childhoodPedagogyDevelopmental psychologyGender studiesSociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This study examined how multicultural parents, including marriage migrants, North Korean refugees, and immigrant laborers, perceive their childrens education and their participation in the South Korean early childhood educational system. The findings showed that the parents were particularly concerned about their childrens possible problems related to peer interaction and bullying, They additionally believedthat South Korean mothers biased thinking toward multicultural people have caused their children to have biased attitudes against multicultural peers. Due to the concern, some parents even hid their childs cultural and ethnicalbackground in the school. The parents also argued that South Korean educators often insist on ``our`` or ``South Korean`` ways to the parents and thus often encountered difficulties dealing with the school system. This implies the need for South Korean educators to reflect critically on their current practices and provide more transformative approaches of multicultural education to develop students and parents understanding of diversity.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0030.003
Open science0.0000.004
Research integrity0.0010.002
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 head0.023
GPT teacher head0.316
Teacher spread0.292 · 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 designQualitative
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

Citations2
Published2012
Admission routes1
Has abstractyes

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