Use of Law by US States During the COVID-19 Pandemic With Respect to People Who Were Undocumented
Bibliographic record
Abstract
Objectives. To systematically identify and analyze US state-level legislation concerning people who were undocumented during the COVID-19 pandemic, from January 2020 through August 2021. Methods. Using standard public health law research methods, we searched Westlaw’s online database between November 2021 and January 2022 to identify legislation addressing COVID-19 and people who were undocumented. We abstracted relevant information, analyzed the data, and identified primary themes for each bill and resolution. Results. Sixty-six bills and resolutions, from 13 states, met the inclusion criteria. Legislation addressed 5 primary themes: eligibility and access to health-related services (n = 16), health and personal information (n = 10), housing assistance (n = 13), job security and employment benefits (n = 14), and monetary assistance (n = 13). Conclusions. Approximately one quarter of state legislatures introduced bills or resolutions regarding people who were undocumented and COVID-19. State-level laws are an important tool to mitigate the disproportionate impact of public health emergencies on vulnerable groups. Public Health Implications. As states shift attention away from the exigencies of COVID-19, this research provides insight into how law might be used to protect those who are undocumented throughout the full cycle of future public health emergencies. (Am J Public Health. 2022;112(12):1757–1764. https://doi.org/10.2105/AJPH.2022.307090 )
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".