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Record W3176857863 · doi:10.4018/ijbide.2021070103

Plurilingual STEAM and School Lunches for Learning?

2021· article· en· W3176857863 on OpenAlexaff
Daniel Roy Pearce, Mayo Oyama, Danièle Moore, Yuki Kitano, Emiko Fujita

Bibliographic record

VenueInternational Journal of Bias Identity and Diversities in Education · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGrassrootsExperiential learningOpenness to experienceReflexivityPedagogyNature versus nurtureDiversity (politics)MultilingualismCultural diversityForeign languageSociologyPsychologyPolitical scienceSocial scienceAnthropology

Abstract

fetched live from OpenAlex

In Japan, where there is a bias toward English-only in foreign language education, there are also grassroots efforts to introduce greater plurality in the classroom. However, introducing diverse languages and cultures into the classroom can lead to folklorization, the delivering of essentialized information in pre-packaged formats, which can potentially delegitimize other languages and cultures. This contribution examines a collaborative integrative plurilingual STEAM practice at an elementary school in Western Japan. In the ‘school lunches project,' the children experience various international cuisine, leading up to which they would engage with related languages and cultures through collaboratively produced plurilingual videos and museum-like exhibits of cultural artifacts. The interdisciplinary, hands-on, experiential learning within this project helped the children to develop an investigative stance toward linguistic and cultural artifacts, nurture a deeper awareness of languages and openness to diversity, foster reflexivity, and encourage interdisciplinary engagement.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.462
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.302
Teacher spread0.268 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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