Immigrants, Multiculturalism, and Expensive Cultural Tastes: Quong on Luck Egalitarianism and Cultural Minority Rights
Bibliographic record
Abstract
Kymlicka has offered an influential luck egalitarian justification for a catalogue of polyethnic rights addressing cultural disadvantages of immigrant minorities. In response, Quong argues that while the items on the list are justified, in the light of the fact that the relevant disadvantages of immigrants result from their choice to immigrate, (i) these rights cannot be derived from luck egalitarianism and (ii) that this casts doubt on luck egalitarianism as a theory of cultural justice. As an alternative to Kymlicka’s argument, Quong offers his own justification of polyethnic rights based on a Rawlsian ideal of fair equality of opportunity. I defend luck egalitarianism against Quong’s objection arguing that if choice ever matters, it matters in relation to cultural disadvantages too. Also, the Rawlsian ideal of fair equality of opportunity cannot justify the sort of polyethnic rights that Quong wants it to justify, once we set aside an unwarranted statist focus in Quong’s conception of fair equality of opportunity. Whatever the weaknesses of luck egalitarianism are, the inadequacy of the position in relation to accommodating cultural disadvantages of immigrants is not among them.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".