Bibliographic record
Abstract
In this article, I argue that there are good reasons to permit states to engage in their own forms of prioritization of refugees for admission, if doing so enables more refugees overall to find safety. I identify three distinct clusters of programs that states operate, those that emphasize contribution-based reciprocity, those that emphasize anticipated benefit, and those that elevate cultural considerations. I assess the legitimacy of these programs separately, and then consider them together, to defend the view that – overall, considering the profound need for resettlement spots – they warrant our normative support. At least at first glance, the reason that these programs are legitimate is that they are all underpinned by principles in moral philosophy that in general command widespread support. In the final section, I examine the discomfort they generate, and suggest that the discomfort has three related sources: (i) that there is something wrong with considering the benefits refugees may bring when at issue is simply whether states are carrying out their moral duties towards them; (ii) that the result of permitting such prioritization is that those who are most vulnerable are left behind; and (iii) that cultural considerations can easily slide into racist considerations. While I agree that we must be attentive to how these programs operate in practice, and whether they generate harm (in particular, more harm than good), I believe that in the current context, these objections can be adequately countered so that we should in general offer these programs our cautious, and conditional, support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| 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".