Open hearts and open access for immigrant professionals [microform] : a case study of Chinese immigrant engineers in the Greater Vancouver region, British Columbia, Canada
Bibliographic record
Abstract
Successful economic integration benefits both recent skilled immigrants and Canada. Researchers have indicated that human capital, social capital and gender all affect the economic integration of skilled immigrants. Few studies, however, examine the association between post-immigration human capital development and occupational attainment. Little is known about how professional regulatory bodies, immigration services agencies and educational institutions affect the development of social capital once individuals have arrived at their immigration destination. There is also limited knowledge about how female skilled immigrants overcome dual barriers as "immigrants" and "females" to achieve occupational attainment. In this dissertation I draw on data collected through interviews on the employment experiences of 23 Chinese-Canadian immigrant engineers who live in Vancouver, Canada. My findings indicate that human capital is not static; rather it undergoes continuous development in response to changes in the skills deemed important from one labor market to another. Financial support from Canadian governments is needed in order to raise the economic returns of post-immigration human capital. I also find that institutional involvement helps Chinese-Canadian immigrant engineers acquire social capital. The coordination between key stakeholders will facilitate economic integration of skilled immigrants. Finally, my study indicates that female Chinese-Canadian immigrant engineers actively forge and mobilize social resources in order to successfully develop their careers in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".