Climbing a Ladder: Employment Mismatch Experiences of Visible Minority, Skilled Immigrants in Canada
Bibliographic record
Abstract
Visible minority, skilled immigrants face employment mismatch, a significant labour market barrier in Canada. Using semi-structured interviews, this qualitative study delves into factors contributing to employment mismatch during immigrants' first 15 years in Canada. Study participants identified several factors namely, Canadian work experience as a prerequisite for employment, lack of recognition of education and work experience obtained abroad, guidance provided by settlement agencies, practical considerations for survival, insufficient language skills, and challenges related to gender roles. Consequences of employment mismatch included frustration, anger, discouragement, and embarrassment. To confront employment mismatch, participants implemented strategies: viewing employment mismatch as a steppingstone, obtaining further education, and discovering new careers in Canada. This research contributes to the existing literature by presenting a composite description of participants' thoughts and feelings, revealing how they experienced and interpreted the phenomenon of employment mismatch, while persevering to enter and advance in the labour market.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".