Anti-Immigrant Rhetoric and Policy as Manifestations of Structural Racism—Implications for Advancing Health Equity
Bibliographic record
Abstract
Recent work by researchers and policy makers has argued that solutions to racial inequities in health must target manifestations of structural racism, such as barriers to economic mobility, high-quality education, health care, and high-paying jobs, and the context and policies that allow racial inequities to persist. 1 Despite this long-overdue reckoning with the pervasiveness and consequences of structural racism in the United States, there are significant challenges to conducting research on discriminatory rhetoric and policies that target particular groups and are associated with health outcomes.Both the 2016 presidential election and subsequent Trump administration were characterized by anti-Muslim and anti-immigrant rhetoric as well as corresponding policy actions targeting individuals from Muslim-majority and Latin American countries, most notably Executive Order (EO) 13669, commonly referred to as the "Muslim Ban."In an innovative analysis, Samuels and
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.069 | 0.053 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".