Policy hypocrisy or political compromise? Assessing the morality of US policy toward undocumented migrants
Bibliographic record
Abstract
Introduction Immigrant receiving countries like the USA frequently profess their desire to keep out undocumented migrants. They use strong rhetoric to convey this to those inside and outside the state, and they adopt policies aimed at doing so. Yet, many of these policies are either known to be deficient or are only selectively enforced. The USA for example ‘cracks down’ on undocumented migration with methods known to be generally unsuccessful in deterring migration, and all the while not addressing what is referred to by experts as the ‘linchpin’ of migration control: employer demand. In short, many aspects of immigration policy, especially those policies directed at undocumented migrants, display a high degree of hypocrisy. How do we assess our policies directed at undocumented migration from a moral standpoint? Are our immigration policies, or our selective enforcement of them, by definition immoral because they are knowingly, even at times intentionally, designed to obscure – in this case most often to convince the public that something is being done to stop undocumented migration when in reality government actions are half-hearted and intended to appease many different audiences of which a generally restrictionist public is just one? Most of us would want to answer in the affirmative – the hypocrisy is by definition immoral. Furthermore, theorists of ethics looking at migration also tend to agree that our policies toward undocumented migrants are morally questionable.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".