East Asian Heritage Language Education for a Plurilingual Reality in the United States: Practices, Potholes, and Possibilities
Bibliographic record
Abstract
Drawing on research about East Asian (mainly Chinese, Korean, and Japanese) heritage language (HL) teaching and learning in three contexts—the home, community heritage language schools, and programs in U.S. K–12 schools—this article discusses the challenges that East Asian subethnic groups face in improving HL education in each context. Specifically, the review finds that in the home context, parents’ practices in HL maintenance are complicated by factors such as parents’ attitudes and beliefs about language maintenance and literacy resources. While community language schools have been recognized as the strongest efforts for teaching HLs, these schools often face various challenges in getting the human and physical resources they need. Finally, the review reveals the lack of a supportive environment for HL maintenance in K–12 schools. The findings suggest an urgent need for realignment among federal policies, mainstream school curricular, and community practices in order to maximize the full potential of the United States becoming multilingual in a globalized society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".