The Use and Misuse of `National Security` Rationale in Crafting U.S. Refugee and Immigration Policies
Bibliographic record
Abstract
Since the terrorist attacks of 11 September 2001, U.S. immigration and refugee policy has developed based on narrow and evolving theories of `national security`. Immigration reform legislation, federal regulations, and administrative policy changes have been justified in terms of the nation`s safety. On 1 March 2003, the U.S. Immigration and Naturalization Service (INS) was folded into the massive new U.S. Department of Homeland Security (DHS), formally making immigration a homeland defense concern. Counterterror and immigration experts increasingly agree on what constitute effective and appropriate immigration policy reforms in light of the terrorist threat. Unfortunately, many of the post-September 11 policy changes do little to advance public safety and violate the rights of refugees and asylum seekers. These include reductions in refugee admissions, the criminal prosecution of asylum seekers, the blanket detention of Haitians, and a safe third-country asylum agreement between the United States and Canada. Other measures offend basic rights and may undermine counterterror efforts. These include `preventive` arrests, closed deportation proceedings, and `call-in` registration programs. This article reviews post-September 11 U.S. policy developments based on their impact on migrant rights and their efficacy as counterterror measures. It argues for a more nuanced and rigorous sense of `national security` in crafting refugee and immigration policy.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".