Cultural differences - The role it plays in skilled Chinese immigrant underemployment
Bibliographic record
Abstract
This paper approaches the problem of skilled immigrant underemployment at the human resource professional level by focusing on the relationship between communications disconnects caused by cultural differences and hiring decisions.It was confirmed that within the Vancouver tech industry, only a small percentage of skilled Asian immigrants applying get hired with the two most commonly cited factors affecting employers decision to hire being communication skills and the ability to work within the Canadian context.Employers were aware that they are turning away skilled Asian immigrants, but felt it was a 'business decision' based on perceived negative effects on productivity.Employers were concerned with not only the cost, but also the knowledge required for successful integration.Although there is a definite need for cultural integration programs, it may be unrealistic to expect employers to provide them and there may be a role for educational institutions to help fill this void.institutions to help fill this void.Sixty-seven percent of employers interviewed said that they would consider outsourcing cultural integration programs and 83 percent saw a need for educational institutes to offer cultural integration courses.David Thomas for offering continual guidance and advice from the development of the initial research question to project completion.Kenny Zhang at the Asia Pacific foundation and Don DeVoretz at RllM for helping me discover what has been done in the past and find a place to start.Kirk Hill and Melissa McRae from SFU career services for their support and ideas.Rosalie Tung
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".