Non-investment, the Lack of English Fluency of Well-educated Professional Chinese Immigrants in Anglophone Canada
Bibliographic record
Abstract
The Chinese are the largest ethnic minority in Canada. As a group, they are well-known for not being able to speak fluent English, including those well-educated individuals who immigrated to Canada mainly in the 2000s. There is a rich literature in applied linguistics about immigrantsâ second language learning. Nevertheless, studies on second language practice of this particular group of well-educated Chinese immigrants are lacking. This enquiry is aimed at exploring the reasons why well-educated professional Chinese immigrants, who constitute a large portion of the Chinese population in Canada, do not put more effort into improving their English after settling down there, even though a better level of proficiency can bring apparent benefits to their economic and social success in the new host country. \n \nNineteen well-educated professional Chinese immigrants took part in in-depth interviews, the sole method of data collection of this exploratory study which has a conceptual framework capitalizing on such concepts as motivation/demotivation, value, capital, investment, community and identity. The findings reveal that the principal reason for a dearth of efforts is that they do not deem such efforts very necessary and worthwhile. \n \nThe contribution of this study to knowledge lies in the conceptualization of non-investment, which complements the existing notion of investment by incorporating into it motivational/demotivational factors that the latter dismisses, and which addresses the issue as to what resources an individual depends on when making investment decisions. In addition, this concept is also a contribution to the under-researched area of demotivation. \n \nThe immigration of well-educated Chinese professionals to Canada is one of the trends in human migration on the global scale which is a part of globalization. Therefore, the comprehension of the rationale behind their second language practice is significant to the applied linguists who work in the realm of globalization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".