Labour market experiences of recent dependent skilled female immigrants in second-tier cities in Ontario, Canada
Bibliographic record
Abstract
The geographical dispersal of immigrants has been a priority of immigrant settlement policies in Canada. Considerable progress, including the founding of a federal-provincial-municipal partnership, has been made in this direction. However, skilled immigrants continue to experience barriers in entering and progressing in the labour markets of rural areas and smaller communities; in particular the female spouses of the male principal applicants in the skilled workers program are experiencing these barriers. The results presented in this research suggest that the second-tier cities Guelph, Kitchener, and Waterloo have been successful in attracting skilled immigrants largely due to the universities and the manufacturing sector. However, the surrounding rural areas, lacking similar employment opportunities, have failed to attract skilled immigrants. These areas are apparently not benefiting from the higher-education infrastructure that is converging on second-tier cities. Even in Guelph, Kitchener and Waterloo, the retention of immigrants is not guaranteed as many skilled female spouses of skilled male immigrants are failing to be fully integrated into the labour market. The participants in this study suggested that the barriers for "dependent" skilled female immigrants to enter the labour market outweighed the opportunities. Consequently, many dependent skilled female immigrants are either unemployed, underemployed, or they are withdrawing completely from the labour market. Many dependent female spouses sampled in this research were underemployed and dissatisfied with the labour market situation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".