Plurilingual STEAM and School Lunches for Learning?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".