Geo‐scripts and refugee resettlement in Canada: Designations and destinations
Bibliographic record
Abstract
Most immigrants to Canada who are not refugees contribute to decisions about where they settle; resettled refugees do not. This paper illustrates how one's designated category of resettlement decisively shapes the place one begins life in Canada, and how each has a specific geographical trajectory—or geo‐script. The geo‐scripts are distinct for each category of resettlement: privately‐sponsored refugees live in the same community as the volunteers who finance and support them; government‐assisted refugees are “destined” to one of more than two dozen cities with federally‐funded refugee‐specific services; and blended visa office‐referred refugees are supported through a shared funding model between government and sponsors near whom they co‐locate. Geo‐scripts are derived from refugee categories that effectively govern the spatial settlement patterns of refugees and, in turn, shape the opportunities and outcomes of the resettlement process. Government data show that 70% of resettled refugees do not move after they arrive in Canada. The geo‐scripts of resettlement thus shape people's lives and livelihoods in Canada in fundamental ways. Drawing on interviews conducted between 2017 and 2021 with formerly resettled refugees living in four provinces who have lived in Canada for more than 20 years, locational decisions to stay or leave one's initial destination are elaborated .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".