Bibliographic record
Abstract
Abstract Skill-selective immigration policies, through which states favor the admission of highly skilled migrants over low-skilled migrants, are a familiar component of the immigration landscape. Wealthy Western states, such as the United States, United Kingdom, Canada, and Australia, have explicitly declared their desire to attract the “best and the brightest.” On the other hand, attitudes toward low-skilled migrants could not be more different. They have consistently been portrayed as dangerous and undesirable, a drain on social welfare, and economically threatening to citizens. This book argues that we ought to rethink this stance. Beginning from the widely shared principle of equal respect for all persons, it proposes that equal respect requires the recognition of each person’s pro tanto right to social equality, regardless of their citizenship status. Even if states have the right to exclude noncitizens, they cannot do so in a way that is demeaning or subordinating to excluded persons. The right to social equality gives us a richer picture of why certain instances of immigrant selection, such as the United States’ recent ban on citizens from Muslim-majority countries, are unjust. However, it also has troubling implications for skill-selective immigration policies, as they are currently practiced: the book reveals that they ought to be regarded as a form of wrongful discrimination. Drawing on the framework of social equality, the book goes on to consider the problem of colonial injustice and how it may be reproduced by skill-selective immigration policies, as well as migratorial disobedience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".