Selecting Refugees for Resettlement to Norway and Canada: Vulnerability, Integration and Discretion
Bibliographic record
Abstract
Abstract This chapter examines how the concept of vulnerability is “translated” from legal bureaucratic discourses into actual policy and practice in the refugee resettlement context. In particular, we trace how the integration potential of refugees continues to be weighed against their vulnerabilities in the process. While resettlement is a voluntary commitment and not legally binding, states that have signed the 1951 Geneva Convention have agreed to share the responsibility of providing protection and solutions for refugees who cannot return to their country of origin. Through a comparative discussion of refugee resettlement in Canada and Norway, we shed light on some mechanisms through which the humanitarian focus on prioritizing the most vulnerable comes under pressure from competing political considerations and rationales. By examining instances of what we call the political or ‘tactical’ uses of resettlement, we aim not only to highlight its partisan and domestic political dynamics but also to open up questions of who is ultimately left behind and considered ‘too vulnerable’ for resettlement.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".