On Critical Localism and the Privatisation of Refuge: The Resettlement of Syrian Newcomers in Canada
Bibliographic record
Abstract
Abstract Increases in displacement and forced migration is an enduring feature of many countries. Resettlement is a policy response to displacement, that relocates refugees from a country of asylum to a safe third country. Canada’s Private Sponsorship of Refugees Program is noteworthy. It allows non-profit organizations and volunteer groups to support newcomers during their first year in Canada and has especially aided resettlement of Syrian refugees on an international scale. We take a critical look at this programme by focusing on the social implications of private sponsorship and Syrian newcomers’ experiences of resettlement. We view private sponsorship initiatives as furthering processes that privatise decision-making, identify specific sponsorship groups as objects of policy, and transfer public authority to private citizens and non-profit organizations to encourage refugee resettlement. We argue that the privatising processes defining private sponsorship are further complicated within localised settings. Based on scholarly, policy, and programme documents, and extensive semi-structured interviews with Syrian newcomers in southern Ontario, Canada, we illuminate what we call “localising the privatisation of refuge,” which calls attention to the various networks, activities and relations of power that define and shape the local, and the processes and experiences of refuge that take place within.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.033 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".