Exploring Refugees’ Labour Market Experiences, Economic Success, and Integration Trajectories in Canada: The Implications for Achieving Social Justice
Bibliographic record
Abstract
Using the 2016 Canadian census data, which is for the first time linked with administrative data on tax and immigration, this study examines the labour market outcomes of two groups of resettled refugees, Government-Assisted Refugees (GARs) and Privately Sponsored Refugees (PSRs), within a social justice framework. While previous research has focused on employment status and earnings, this study included occupational status as well to broadly assess the economic integration of refugees resettled in Canada. This study focused on four factors, gender, admission category, location of study, and age of arrival in Canada, which have received limited attention in previous refugee research. While existing research demonstrates the poorer economic outcomes of refugees compared to economic immigrants, the findings of subcategories of resettled refugees are descriptive, dated, and scant. My dissertation research reveals that nearly half of resettled refugees completed a postsecondary certificate, diploma, or degree, and over 65 percent of postsecondary graduates received their highest education in Canada. Yet, one third of resettled refugees were inactive in the labour market. While refugees are not admitted to Canada based on their financial or human capital, a considerable number of resettled refugees tended to seek self-employment instead of paid work. Refugee women were more disadvantaged in the Canadian labour market than refugee men. While PSRs fared better in finding a job and earned more than GARs, the picture is reverse regarding occupational status. A significant number of resettled refugees with foreign postsecondary credentials were working in low paying jobs, and the return on foreign university degrees was significantly lower than that on Canadian degrees. Age of arrival in Canada has an unanticipated association with employment status and employment income. My dissertation research offers new insights about the challenges and barriers that refugees face to access employment, desirable occupations, and adequate earnings. The findings suggest the need to reduce the burden of economic integration on refugee individuals with a positive peacebuilding and social justice lens. This research contributes to the broad Canadian immigrant integration literature, and advances the literature on social justice and refugee integration within the global north host countries’ contexts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".