Formal and informal support networks as sources of resilience and sources of oppression for temporary foreign workers in Canada
Bibliographic record
Abstract
Abstract In this article, we explore temporary foreign workers’ (TFWs) access to and experiences with formal and informal supports in Canada. Our study utilized a participatory action research design and four overlapping phases of data collection: individual interviews with current and former TFWs, focus groups, individual interviews with settlement service agencies, and a cross-sectional survey with current and former TFWs. We used an intersectional theoretical framework to analyze these data and explore ways that TFWs interact with formal and informal sources of support for navigating their precarious immigration status and integration in Canada. Our findings show these supports have the potential to both benefit and harm TFWs, depending on their social positioning and availability of institutional resources. The benefits include information that aids settlement and integration processes in Canada, while the harms include misinformation that contributes to status loss. Future research and policy should recognize the complexity of informal and formal support networks available to TFWs. An absence of government support is apparent, as is the need for increased funding for settlement service agencies that serve these workers. In addition, Canada should better monitor employers, immigration consultants, and immigration lawyers to ensure these agents support rather than oppress TFWs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".