Syrian refugee resettlement and the role of local immigration partnerships in Ontario, Canada
Bibliographic record
Abstract
We examine Canada's recent Syrian Refugee Resettlement Initiative (SRRI) paying close attention to the resettlement role played by mid‐sized urban communities. We elaborate on a key policy dimension at work at this scale of action: local immigration partnerships (LIPs). We start with a very brief review of Canada's history of mass refugee resettlement. Second, we assess the policy of LIPs, particularly how they have been presented as a form of “place‐based policy,” and third, we offer an overview of the role the LIPs played in three case study communities (Hamilton, Ottawa, and Waterloo) during the SRRI. Finally, we present three overarching themes that emerged from our research in each of these communities: the importance of each community's history of immigration and refugee resettlement; the embeddedness of the LIP and its leadership in the local community; and how the positioning of each LIP relative to the three levels of government and its official Resettlement Assistance Program agreement holders impacted its ability to act. The history, location, and place characteristics of each community influenced the nature of intersectoral and intergovernmental relations in distinctive ways, and differentially shaped the effectiveness of each LIP's ability to contribute to the SRRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".