Syrian refugee resettlement: A case study of local response in Hamilton, Ontario
Bibliographic record
Abstract
This paper examines the response by local government and stakeholders to the arrival and resettlement of Syrian newcomers in Hamilton, Ontario in 2015 and 2016—the first major wave of refugee arrivals since two significant changes in Hamilton's settlement organizational landscape. The creation of a local immigration partnership called the Hamilton Immigration Partnership Council (HIPC) is an example of place‐based policymaking within local immigration and settlement in Canada. Place‐based approaches emerged to bypass top‐down policy ineffectiveness, and the shift to empower civic participation in the local decision‐making process is seen as one solution to public policy innovations. Examination of HIPC's role in this context is thus critical to understand the challenges and learnings encountered in one place‐based setting. Our findings suggest that the lack of power (in terms of information, communication, resources, and funding) led to a missed opportunity for HIPC to lead a significant resettlement initiative. HIPC's inability to bring together key partners across the sector prior to and during the event is symptomatic of systemic barriers the Council had faced, including competing interpretations of HIPC and its role by its members. This study suggests the effectiveness of place‐based policy is not without its nuances, and iterative challenges and learnings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".