Immigrant Selection, Health Requirements, and Disability Discrimination
Bibliographic record
Abstract
Australia, Canada, and New Zealand currently apply health requirements to prospective immigrants, denying residency to those with health conditions that are likely to impose an “excessive demand” on their publicly funded health and social service programs. In this paper, I investigate the charge that such policies are wrongfully discriminatory against persons with disabilities. I first provide a freedom-based account of the wrongness of discrimination according to which discrimination is wrong when and because it involves disadvantaging people in the exercise of their freedom on the basis of morally arbitrary features of their identity. Discrimination is permissible, I suggest, when it is necessary to advance a valuable exercise of the discriminating agent’s freedom. I then apply this account to the case of social cost health requirements. Against critics of these requirements, I argue that it is sometimes permissible for states to discriminate against prospective immigrants with disabilities. States may do so, I suggest, when such discriminatory treatment is necessary to prevent an increase in rates of mortality and/or morbidity among citizens. Alongside critics of social cost health requirements however, I argue that existing policies are likely a form of wrongful discrimination insofar as they are too broad to satisfy this standard.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".