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Record W3123231591 · doi:10.4018/ijbide.2020070101

Plurilingualism and STEAM

2020· article· en· W3123231591 on OpenAlexaff
Daniel Roy Pearce, Mayo Oyama, Danièle Moore, Kana Irisawa

Bibliographic record

VenueInternational Journal of Bias Identity and Diversities in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMultidisciplinary approachContext (archaeology)Openness to experienceReflexivityPedagogyNature versus nurtureDiversity (politics)SociologyPolitical scienceEngineering ethicsPsychologySocial scienceGeographyEngineering

Abstract

fetched live from OpenAlex

This contribution attempts to clarify the relationship between the practice of plurilingual education and STEAM (interdisciplinary pedagogy that incorporates science, technology, engineering, art, and mathematics) through the lens of peace learning at an elementary school in Japan. Japan has a rich history of peace education, although it has received limited focus in the international literature, whereas plurilingual education remains relatively unknown in the country. Within this context, the article examines a teacher-initiated plurilingual and intercultural project focused on a multidisciplinary approach to peace learning. Analyses of multimodal data, including video recordings, photographs, researchers' field notes, learners' journals, and semi-structured reflective interviews, will demonstrate how even within a highly homogenous context, practitioners can promote transferable skills and nurture a deeper awareness of language and openness to diversity, foster reflexivity, and encourage multidisciplinary engagement through plurilingual education, dialogue, and storying.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.026
Scholarly communication0.0050.006
Open science0.0010.014
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.069
GPT teacher head0.372
Teacher spread0.302 · 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
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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Bias Identity and Diversities in EducationSame topicPeace and Human Rights EducationFrench-language works237,207