Transition into the workforce : economic integration of former government assisted refugees and former refugee claimants
Bibliographic record
Abstract
While research in the sphere of settlement and integration is wide in scope and subject, it largely focuses on the labour market outcomes and economic integration of skilled immigrants. Such research exemptions do not capture the economic integration of other immigrant classes such as refugees. In light of such research gaps, this study aimed to examine the economic integration of former Government Assisted Refugees (GARs) and former refugee claimants in Hamilton, Ontario. Through a series of four interviews with former GARs and former refugee claimants who are working within Hamilton's social service sector, this study found that the experiences of refugees can be captured by a combination of human capital and social capital frameworks. Similar to skilled immigrants, refugees are better able to transition into professional fields upon enrolling in post-secondary educational institutions, volunteering, and networking with members outside of their own ethno-cultural community. This study also found that immediate settlement supports, offered by the Resettlement Assistance Program, had positive long term affects on the economic integration of GARs. Former refugee claimants did not have such immediate services and as a result had frustrating immediate settlement experiences. It is therefore argued that the RAP mitigates many systematic and structural barriers which otherwise pose as barriers for the economic integration of refugees.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".