Use it or lose it: The problem of labour underutilization among immigrant workers in Canada
Bibliographic record
Abstract
Abstract Canada is widely recognized as a desirable destination for new immigrants and all levels of governments are generally supportive of ambitious immigration targets set to help meet labour demand. Canada's immigration system is based primarily on human capital, selecting the world's most highly skilled newcomers. However, immigrants to Canada have often faced difficulty in attaining labour market outcomes commensurate with their knowledge and experience. In this analysis, we examine the paradox apparent in the Canadian immigration system—the selection criteria attract highly educated and skilled workers, yet many are not able to find employment opportunities that match their abilities—through the lens of the Labour Utilization Framework. Using data from the Canadian Labour Force Survey for the years 2006–2019 inclusive, we explore five different dimensions of skill underutilization or brain waste: involuntary part‐time work, minimum wage work, unemployment, over‐education (i.e., underemployment), and worker discouragement. Our results suggest that on all dimensions of labour underutilization measured in the study, immigrants are overwhelmingly at a disadvantage relative to their Canadian‐born, non‐Indigenous counterparts. We discuss the ethical implications of immigrant brain waste for both individuals and society and conclude by suggesting some possible policy responses to improve the utilization of immigrant talent in Canada.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".