The Impact of COVID-19 on Noncitizens and Across the U.S. Immigration System
Bibliographic record
Abstract
The COVID-19 (the novel coronavirus) pandemic, and the related federal response, disrupted virtually every aspect of the U.S. immigration system. Visa processing overseas by the Department of State, as well as the processing of some immigration benefits within the country by U.S. Citizenship and Immigration Services (USCIS), have come to a near standstill. Entry into the United States along the Mexican and Canadian borders—including by asylum seekers and unaccompanied children—has been severely restricted. Immigration enforcement actions in the interior of the country have been curtailed, although they have not stopped entirely. Tens of thousands of people remain in immigration detention despite the high risk of COVID-19 transmission in crowded jails, prisons, and detention centers that U.S. Immigration and Customs Enforcement (ICE) uses to hold noncitizens. The pandemic led to the suspension of many immigration court hearings and limited the functioning of the few courts which remain open or were reopened. Meanwhile, Congress left millions of immigrants and their families out of legislative relief, leaving many people struggling to stay afloat in a time of economic uncertainty.This report seeks to provide a comprehensive overview of the impact of COVID-19 across the immigration system in the United States. Given that the landscape of immigration policy is changing rapidly in the face of the pandemic, this report will be updated as needed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".