Discrimination and Policies of Immigrant Selection in Liberal States
Bibliographic record
Abstract
How should liberal societies select prospective members? A conventional reading of immigration history posits that whereas ascriptive characteristics drove immigration policy in the past, contemporary policy is based on the principle of nondiscrimination. Yet a closer look at the characteristics of those admitted reveals systematic group biases that run counter to liberalism’s core moral commitments. This article first discusses liberal states’ basic moral obligation to treat their citizens with equal respect. It then identifies ways in which the group biases produced by immigration policy violate that principle, when states either deprive their citizens of fundamental rights or stigmatize them through hierarchical constructions of citizenship. Three mechanisms are presented—structural bias, profiling, and positive selection—by which seemingly liberal admissions policies produce illiberal outcomes. The empirical analysis explores the resulting discriminatory group biases in the context of language and income conditionalities on family migration, excessive demand restrictions against economic migrants, and visa waivers for international travelers. We conclude that immigration reforms that mitigate, if not erase, these morally problematic patterns are within the reach of liberal states.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".