Bibliographic record
Abstract
Teaching heritage language is regarded as an act of social justice, but under what conditions and in what context? This article examines the educational practice of introducing heritage language to Chinese return-migrant children and Japan-born Vietnamese children. The language programs under investigation are conducted in a community education center and in an after-school setting in a public elementary school in a multiethnic neighborhood in Osaka, Japan. This study demonstrates how the local community's practice of heritage language learning dissolves the boundaries among ethnic minorities, bringing together all participants and cutting across ethnic lines. The result is empowering, but with a limited effect. At the same time, the institutionalized practice of heritage language learning at school becomes a marker for ethnic minorities and is used to maintain the boundary between ethnic minorities and Japanese, despite official discourses of minority education for empowerment. Ethnographic data show discrepancies between the views of teachers and communities about what ethnic minorities should be like and what they are hoping to find in Japan. The politics of heritage involves the legitimization of power and distinction, as well as the exclusion of those who do not have access to heritage. Situating each case within the politics of heritage, schooling, and Japan's multicultural initiatives, this article examines what is legitimized and what is excluded through teaching and learning heritage language in both cases and discusses the implications of heritage language teaching for immigrant children in Japan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".