Research on heritage language development and maintenance of Korean
Bibliographic record
Abstract
This chapter critically reviews studies on how Korean as a heritage language is learnt, developed and maintained in the Australian context from broad and comparative perspectives. Relevant studies conducted in Australia were surveyed and examined, and in parallel, they review some related studies conducted in Canada and USA as well. Shin and Jung discuss issues with terminologies of heritage language a well as KHL development and maintenance in the Australian education systems, in the Australian-Korean community, in the family, and at the individual level. They also reaffirm that the Korean language is an important community language in Australia and argue that with the increasing population with Korean heritage and implementations of Korean heritage language courses in some schools and universities together with the growth of Korean language education in Australia, researchers give attention to this area, investigating various KHL topics such as language maintenance and shift, language use and proficiency, and ethnic identity, as well as instruction-related issues such as curriculum planning, pedagogies and resource materials.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".