Parents’ Attitudes Toward Heritage Language Maintenance for Their Children and Their Efforts to Help Their Children Maintain the Heritage Language: A Case Study of Korean-Canadian Immigrants
Bibliographic record
Abstract
In this study we explore Korean immigrant parents’ attitudes toward heritage language maintenance for their children and their efforts to help their children maintain Korean as their heritage language in Montreal. Some implications for mainstream school policies and classroom practices are touched on briefly. Data were collected from nine Korean immigrant parents who had a child (or children) between the ages of 6–18 in 2005, using a questionnaire and interviews. The interviews asked about Korean immigrant parents’ attitudes toward heritage language and cultural identity maintenance for their children and attitudes toward the Korean language, the Korean community, and the Korean churches; four items designed to obtain information about parents’ efforts to help their children maintain the heritage language both at home and outside of the home were also included. The findings suggest that Korean immigrant parents are very positive toward their children’s heritage language maintenance. Korean parents believe that their children’s high level of proficiency in the Korean language would help their children keep their cultural identity as Koreans, ensure them better future economic opportunities, and give them more chances to communicate with their grandparents efficiently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".