Investing for what? A Bourdieusian class perspective on well-educated Chinese immigrants faced with linguistic and cultural barriers in Canada
Bibliographic record
Abstract
Abstract There are many skilled immigrants from mainland China in Canada. Despite being well-educated professionals, they often do not speak fluent English. To understand this sociolinguistic phenomenon, semi-structured interviews were conducted with 19 informants. The results indicate that faced with linguistic and cultural barriers in multicultural Canada, the informants are disadvantaged in the job market and experience social distance from their white local counterparts. They believe that overcoming these barriers requires a substantial investment in English, with no guaranteed return. Therefore, they prefer to invest in areas that provide job security, such as developing their professional skills. These findings are then analysed through Bourdieu’s class perspective: the informants’ positions within the social space of Canadian society and their social trajectory suggest that investing in symbolic capital in the form of the dominant linguistic and cultural competencies is not the most effective path to upward mobility. Instead, they find it more prudent to focus on enhancing their professional skills to strengthen their current positions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".