East Asian Heritage Language Education for a Plurilingual Reality in the United States: Practices, Potholes, and Possibilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it