Employment Equity and Access to Social Welfare for Illegalized Immigrants: An Inclusive Approach That Also Makes Economic Sense
Bibliographic record
Abstract
The global English-language research on illegalized migrants and labour markets is dominated by U.S.-based studies, with limited attention to Canada and Europe. The literature identifies four main findings: Restrictive immigration policies over the past three decades have worsened illegalized immigrants’ labour market outcomes. Labour market impacts show that illegalized immigrants tend to raise earnings for native-born skilled workers, lower earnings for native-born unskilled workers, and have little effect on overall employment rates. Social welfare effects are positive at the national level but mixed at state, provincial, and local levels. Employer benefits stem from exploiting illegalized immigrants’ precarious status, which limits their ability to demand fair pay and conditions. Although these relationships have not been systematically studied in Canada, evidence from the U.S. and Europe suggests key policy directions for industrialized countries: Inclusion over exclusion: Governments should collaborate to create policies that integrate illegalized immigrants as equal members of society, including equitable access to social welfare programs. Fair labour standards: Policies like Canada’s Employment Equity Act should be amended to ensure employers provide fair wages and are held accountable for preventing exploitation. Community support: Until systemic change occurs, governments should fund community-based organizations that help illegalized immigrants access resources and build connections with labour market institutions—models like Chicago’s Latino Organization of the Southwest’s Economic Development Centre show how this can improve employment outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".