Employment disadvantage of immigrants and visible minorities: Evidence from three Canadian surveys
Bibliographic record
Abstract
This thesis examines the workplace disadvantages faced by immigrant and visible minority workers in Canada. Chapter one presents a broad overview of the research context and provides background information on the characteristics of Canadian immigrants and the current state of knowledge on the employment integration of immigrants and visible minorities. It also outlines each of the three studies in the subsequent chapters. Chapter two examines the income growth of newly arrived immigrants using longitudinal data. The results from this chapter indicate that while European immigrants experience a period of 'catch up' early in their Canadian careers, visible minority immigrants do not enjoy such a catch-up. This racial difference in recent immigrants' income growth is found to be caused by the fact that visible minority immigrants receive lower returns to education, work experience and unionization. Furthermore, visible minority recent immigrants face greater penalties for speaking a non-official first language than their white counterparts. Chapter three explores the subjective side of workplace discrimination. In particular, this chapter examines how actual experiences of disadvantage and expectations for equitable treatment influence visible minority workers' perceptions of discrimination at work. The findings from this chapter suggest that while both actual experiences and expectations for equity affect perceptions of discrimination, the latter may be more important than the former. Chapter four looks at post-migration educational investment among newly arrived immigrants and examines the effect of post-migration education on new migrants' labour market integration, as measured by earnings. This chapter finds a positive relationship between pre-migration human capital and post-migration educational investment, but no relationship between financial capital and post-migration education. Furthermore, this chapter finds that post-migration education does indeed improve new immigrants' earnings, particularly for immigrants whose foreign work experience is not recognized in Canada. Chapter five concludes the thesis with a brief synopsis of the empirical analyses. Policy implications and recommendations are also provided in this final chapter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".