Common Factors Contributing to Successful Career Changes Among Chinese Skilled Immigrants in Canada
Bibliographic record
Abstract
Canada is one of the major immigration countries in the world. According to Dolan and Young (2004), Canada has the largest rate of immigration in the world. Every year, the Canadian government accepts millions of immigration applications from people around the world. Many of them are skilled immigrants who bring Bachelor’s, Master’s or Ph.D degrees and valuable professional experience from their home countries. According to a Statistics Canada 2008 survey, 60% of recent skilled immigrants have Bachelor’s degrees compared to 20% of Canadians. They choose to immigrate to Canada to achieve further career opportunities and a better quality of life for themselves and their families. However, life as an immigrant is often not as easy and smooth as they expected. They inevitably face challenges and obstacles. Many immigrants are unable to find employment opportunities in Canada and bitterly give up their immigration dreams to return to their homelands. On the other hand, some skilled immigrants quickly make social and mental adjustments after immigration and successfully launch new careers in their second homeland. Skilled immigrants from China make up 73% of all the new skilled immigrants in Canada (Citizenship and Immigration Canada, 2000). This research will focus on interviewing and collecting research data from Chinese skilled immigrants in order to find out what key factors contribute to successful career changes. It is hoped that the outcomes of this research will provide valuable resources for new skilled immigrants in Canada to be able to efficiently and effectively adjust to the Canadian living environment and achieve career success. This research will also provide a stepping stone for the Canadian Immigration Bureau to develop further skilled immigrants training programs and resources to maximize immigration success rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".