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 immigrants’ 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. Nineteen 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. The 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. The 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".