New Migration Management Policies in the Aftermath of Title 42
Bibliographic record
Abstract
As of mid-2022, an estimated 20 million people were displaced in the Americas. The needs of this massive population are only growing and their migration, safety, and impact on communities in the region is becoming a priority for policymakers, especially in the United States. On April 27, 2023, the U.S. Departments of State (DOS) and Homeland Security (DHS) issued updated policies on migration management across the Western Hemisphere. These policies will be implemented in coordination with regional partners, including the governments of Mexico, Canada, Spain, Colombia, and Guatemala. They are meant to facilitate safe migration across the region, prevent unauthorized crossings and congestion at the U.S. southern border, and create more pathways for people to legally enter the United States and other countries. However, they also put more restrictions on and disqualify many people from accessing asylum; impose harsh consequences for irregular migration; could make access to legal representation more difficult; and may be challenging to implement due to increased staffing needs and existing case backlogs.This policy brief provides an analysis of these new policies, their pros and cons, and the implications and legal precedent they will set for asylum, complementary pathways, and migration management for the United States and other countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".