The Shifting Landscape of International Resettlement: Canada, the US and Syrian Refugees
Bibliographic record
Abstract
The world is today in the grip of the worst forced migration crisis since the end of the Second World War, with tens of millions driven from their homes by conflict. Yet the global system meant to provide protection to the displaced continues to privilege the interests of nation-states rather than those of refugees and has resulted in less willingness by many countries to accept refugees for resettlement. The arrival – actual or potential – of large numbers of refugees and asylum seekers continues to spur a backlash against them, fuelled by fears of security threats, economic costs, and a lack of integration of newcomers. This situation has combined with xenophobia, racism, and broader cultural anxieties and led to a rising tide of nativist populism and even less welcome for those seeking sanctuary. Whereas by the late 1990 s the dominant logic governing refugee protections – at least in name if not in practice – was centred on multilateralism and humanitarian obligations, today there is a more explicit prominence of national interests in refugee policies. In this paper I argue that the continued dominance of nation-state centric priorities is indicative of the fragility of the global refugee regime. I use the example of Canadian and US responses to the Syrian refugee crisis and interviews with officials in each country to illustrate the primacy of national interests rather than international agreements and norms. The US chose to limit and eventually bar most Syrians from resettlement whereas Canada chose to accept a large number over a short period of time. I argue that both cases reveal similar patterns and logics, if not outcomes and an increasing alignment between border controls and immigration policy. I consider what this means for the future of refugee resettlement in North America and for the global refugee regime more broadly.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.041 | 0.019 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 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".