Human and Social Capital as Determinants of Employment Integration among Skilled Immigrants in Canada
Bibliographic record
Abstract
In this study, I have explored the lived employment experiences of skilled, racialized immigrants in British Columbia's labour market. Many studies treat immigrants as if they all have the same experiences, but skilled immigrants—especially those who are racialized—often face unique challenges that are overlooked. This study focuses on skilled immigrants who came to Canada through the Federal Skilled Worker Program and shows how many end up “deskilled,” meaning they work in jobs far below their level of education and experience. Their foreign credentials are often not recognized, community and support systems don’t help them find meaningful work, and gender roles can make these problems even worse for women. While upgrading through Canadian education and having strong social networks can help them stay in their fields, the research highlights how Canada’s promise of equal opportunity doesn’t match the reality many immigrants face. The study calls for better alignment between immigration and labour policies, faster and fairer credential recognition, and employment programs that include mental health and gender-responsive supports.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".