Gender-Based Disparities in the Income of Immigrants in Canada: A Descriptive Analysis
Bibliographic record
Abstract
Canada has one of the world's best immigration policies.However, the poor integration of immigrants into the labour force has resulted in the underemployment of immigrants due to institutionalized discrimination.The discrimination amongst immigrants is likely to vary based on gender.Hence, this study aimed to examine the gender-based disparities in the employment rate and income of immigrants in Canada using secondary data from Canada statistics.Based on the findings from this study, there is a gap in the employment rate (11.0-11.7%) of male and female immigrants.It was also observed that female immigrants earned less than their male counterparts (gender pay gap of 13.5 -49.5%) despite being from the same ethnic group, having similar academic qualifications, and working in similar industries.The findings from this study calls for policy reforms to address institutionalized discrimination against female immigrants in the Canadian labour force.The Childcare support systems in Canada should be enhanced and better flexible working arrangements should be implemented to enable female immigrants attain work-life balance.Lastly, policies that encourage fair hiring processes should be implemented to ensure that employers provide equal job opportunities to women and immigrants.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".