The effect of lack of citizenship on the wages of low-skilled non-permanent resident workers in Canada
Bibliographic record
Abstract
This dissertation explores the effects of lack of citizenship on the wages of low-skilled Non-Permanent Residents (NPRs) in Canada—a category that includes temporary foreign workers, refugee claimants, and people with temporary resident visas on humanitarian grounds. The dissertation uses the 2006 census and quantitative methods (cross-tabulation and regression analysis) to evaluate wage differences between low-skilled workers without citizenship and low-skilled workers with citizenship or permanent resident status. Differences are calculated at the industry sector level and occupation level. The analysis further considers a set of intrinsic characteristics of low-skilled workers (including sex, level of education, official language ability and country of birth) and their occupations (provincial location, rural/urban setting). Empirically, this dissertation confirms that there is a penalty attached to lack of citizenship for low-skilled workers. In absolute terms, low-skilled NPRs earn low wages. In relative terms, these NPRs earn less than both the Canadian-born and immigrants low-skilled workers employed in the same occupations. Among low-skilled NPRs themselves, the Canadian labour market exhibits a hierarchy of wages and labour experiences on the lines of workers' country of birth, province of residence, and rural/urban place of work. Among low–skilled workers born in the same country, wages improve when either citizenship or the rights attached to permanent residence are acquired. From a policy perspective, the dissertation identifies the policy origins and drivers of low wages among low-skilled non-citizens. The thesis makes the case for the relevance of quantitative outcomes analyzed through a critical social lens. From a theoretical perspective too, the dissertation also shows how the state as a biased broker (towards capital) facilitates the implementation of non-citizenship as a means to accessing cheap labour.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".