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Record W4322618947 · doi:10.36534/erlj.2022.02.01

A more-than-language approach to inclusion and success of indigenous children in education: reflections on Cambodia’s multilingual education plan

2023· article· en· W4322618947 on OpenAlexaff
Jessica Ball, Mariam Smith

Bibliographic record

VenueEducational Role of Language Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousInclusion (mineral)PedagogyCurriculumMulticulturalismConceptualizationIndigenous languageSociologyMulticultural educationMultilingualismAction planGovernment (linguistics)Early childhood educationBilingual educationLinguisticsSocial science

Abstract

fetched live from OpenAlex

This article explores the potential of multilingual education (MLE) and culturally sustaining pedagogy to promote school inclusion and success of children who speak a non-dominant language is explored. This potential is examined with reference to the authors’ formative evaluation of the Royal Government of Cambodia’s implementation of a five year Multilingual Education National Action Plan (MENAP). The plan and its subsequent Multilingual Education Action Plan (MEAP) have enabled Indigenous children to be taught using one of five Indigenous languages during the first three years of school. Our interpretation of findings reinforces a conceptualization of MLE as a means to transmit culturally diverse ways of knowing, doing, and being so that children become multilingual and multicultural. This requires a more-than-language approach to MLE whereby nondominant language speakers partner with educators to generate culturally sustaining curriculum content, learning activities and teaching resources that immerse children in the knowledges, thinking, and skills of their own cultural community./ Keywords: Multilingual education, Cambodia, culturally sustaining pedagogy, multiculturalism, Indigenous children

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.358
Teacher spread0.342 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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