Trump Support Explains COVID-19 Health Behaviors in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A wide range of empirical scholarship has documented a partisan gap in health behaviors during the COVID-19 pandemic in the United States, but the political foundations and temporal dynamics of these partisan gaps remain poorly understood. Using an original six-wave individual panel study (n = 3,000) of Americans throughout the course of the COVID-19 pandemic, we show that at the individual level, partisan differences in health behavior grew rapidly in the early months of the pandemic and are explained almost entirely by individual support for or opposition to President Trump. Our results comprise powerful evidence that Trump support (or opposition), rather than ideology or simple partisan identity, explains partisan gaps in health behavior in the United States. In a time of populist resurgence around the world, public health efforts must consider the impact of charismatic authority in addition to entrenched partisanship.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it