Immigration, Citizenship, and Consent: What is Wrong with Permanent Alienage?
Bibliographic record
Abstract
What is Wrong with Permanent Alienage? A migrant's path to citizenship involves two important steps.The first is immigration; the second naturalization.Each step corresponds to an important ethical question.Must foreigners be allowed to immigrate?Must immigrants be offered citizenship?Liberal democratic states offer a different answer to each of these questions.While they continue to jealously guard their power to restrict immigration, they have come to recognise a norm of granting citizenship to legally resident immigrants after a period of residency, usually five to ten years.The borders can be closed, but citizenship must be accessible.Philosophers too tend to separate the two questions.Those defending immigration restrictions acknowledge a duty to naturalise. 1 Even Joseph Carens, the most prominent advocate of open borders, treats the two questions as distinct.He makes his argument for naturalization while assuming, for the sake of argument, that immigration restrictions are permissible. 2ere is then disagreement as to whether foreigners must be admitted but broad agreement that those foreigners who are admitted must, at some point, be offered citizenship.There is, moreover, a consensus that the immigration question and Various drafts of this article have been presented to audiences at EUI, Helsinki, Ottawa and Oxford, receiving extremely helpful feedback on each occasion.I would like to thank the organisers of these events,
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.101 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".