Refugee economic integration and its Implications for Social Justice in Canada
Bibliographic record
Abstract
Refugees--unlike non-refugee immigrants--are forced to flee from their homes, seek protection and navigate potential options for refugee settlement. At post-migration stage, refugee challenges continue as they seek integration in their new setting. While the successful integration of newcomers is expected by newcomers and their host nations, many host countries, governments, and citizens are concerned about how refugees fare economically. With the pre-migration experiences of forced displacement, dispossession, and violence caused by war or other protracted conflicts, government assisted refuges (GARs), as well as privately sponsored refugees (PSRs), blended visa office referred refugees (BVORs) or refugee claimants (asylum seekers) have travelled to Canada to make a new home. Economists, sociologists and the government have paid significant attention to the economic integration of immigrants, yet very few studies have focused exclusively on refugees. Existing literatures demonstrate the poorer economic outcomes of refugees in compared to immigrants and native-born in Canada. This centered in peace and conflict studies (PACS) examines the economic experiences of resettled refugees exclusively employing the 2016 Canadian Census dataset using a social justice framework based on three overlapping theoretical constructs --- structural violence, segmented assimilation, and structuration. The study analyzes employment income, employment status, and education-job mismatch based on a sample of GARs and PSRs within 25-64 years of core working age who have been admitted between1980 and 2016. This paper contributes to the broad Canadian immigrant and integration literature and fills the void in the PACS literature on social justice and refugee integration as most studies are from sociologists and economists.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".