The Ethics of Citizen Selection of Refugees for Admission and Resettlement
Bibliographic record
Abstract
Abstract The global space is in need of creative solutions to the challenges posed by those seeking, and deserving of, asylum. In some democratic states, experiments in permitting citizens to have a greater role in selecting refugees for admission are underway; in this article, I consider the conditions that must apply to any citizen‐selection scheme, in order for such a scheme to be morally acceptable. I begin with an account of the way in which citizen‐selection schemes – usually called ‘sponsorship programs’ – operate presently. I then offer a justification for engaging citizens in refugee resettlement in general, as well as in selecting specific refugees for admission in particular, and then identify several conditions that must attend any permissible citizen‐selection scheme. I defend this account from two objections: (1) that states should be the primary, and indeed only, agent that selects refugees for admission and (2) that citizens will inevitably use problematic criteria in selecting refugees for admission, so they should be denied the right to do so. I conclude with some proposals for how a citizen‐selection scheme can be crafted to respond to this latter worry, including an outline of an exception clause that permits citizens to make the case that some refugees ought to be resettled, even if not specially selected by the UNHCR for resettlement priority, including especially family members and friends.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".