Bibliographic record
Abstract
Abstract:\nImmigration is a hot button issue about which Americans have sent a clear message. They prefer not to admit more aliens until the government is able to credibly screen for entrants who will abide by the terms of admission and sanction those who do not. While immigration debates now focus almost entirely on undocumented workers, they have overshadowed another critical, yet poorly understood challenge: designing institutions to properly screen for aliens who are visa-compliant and sanction non-compliant aliens. Because failed guest worker programs unquestionably increase the size of the undocumented population, this article addresses the difficulty of institutional design by analyzing the highly controversial guest worker provisions of the Immigration and Nationality Act. This article presents original data from a study of visa-compliance decisions of Jamaicans who work in Canada under a program in which screening is precise, sanctioning is effective and compliance is high. On the basis of this study, this comparative immigration law project contends that the United States should partially outsource screening and sanctioning to source-labor countries.\nThis article critiques the historical uni-national approach to immigration law. This approach fails to recognize that there are critical asymmetries between the United States and the countries from which aliens originate in their capacity to gather information about potential entrants and to sanction visa-violators. This recognition leads to the following insight: source-labor countries are often better placed to screen because can access accurate information about potential entrants from their communities. Source-labor countries are also often well-placed to deter non-compliance because through collective sanctioning, they can influence communities of origin to persuade their members to abide by visa terms. The criminal law scholarship regularly recognizes the impact of norms on deterring crimes; this ethnographic study suggests that the same may be true with respect to immigration violations. This article contends that aliens are more likely to be compliant so long as the authorities design legal rules that augment compliance norms already present in source-labor communities and incentivize community members to reinforce them.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".