Mandarin Chinese Heritage Language Learners’ Motivations and Experiences at the Post-secondary Level in Canada Through Language Ideology
Bibliographic record
Abstract
This study investigates how language ideology shapes university Mandarin Chinese heritage language (CHL) learners’ learning motivations and experiences in Canada. Most research investigates motivation through quantitative methods to categorize Heritage Language Education (HLL) into different groups, which neglects their complexity and diverse profiles. Therefore, in this study, individual semi-structured interviews were conducted to collect data and the data were analyzed to capture part of the dynamic and various profiles of Mandarin CHL learners to better support their learning and deconstruct the common idea that “China”, “the Chinese”, and “the Chinese language” are all static and monolithic categories. In this study, participants reported that their primary learning motivations were familial and social connections, potential benefits from Mandarin proficiency, and self-growth. Students who participated in this study also shared their enrollment and unpleasant experiences when they started Mandarin learning in kindergarten or elementary school, and their transformative positive experiences in university mainly due to the change in their social and learning environment. This study also provides rich information about participants' language ideology change to better support future curriculum and program development, and consistent Mandarin Chinese heritage language learning for different stakeholders.Keywords: Learning motivation, Mandarin, Chinese, university heritage language learners, learning experiences, language ideology
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".