Political and Social Correlates of Covid-19 Mortality
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
What political and social features of states help explain the distribution of reported Covid-19 deaths? We survey existing works on (1) state capacity, (2) political institutions, (3) political priorities, and (4) social structures to identify national-level political and social characteristics that may help explain variation in the ability of societies to limit Covid-19 mortality. Accounting for a simple set of Lasso-chosen controls, we find that measures of interpersonal and institutional trust are persistently associated with reported Covid-19 deaths in theory-consistent directions. Beyond this, however, patterns are poorly predicted by existing theories, and by arguments in the popular press focused on populist governments, women-led governments, and pandemic preparedness. Expert predictions of mortality patterns associated with state capacity, democracy, and inequality, do no better than chance. Overall, our analysis highlights the challenges our discipline's theories face in accounting for political responses to unanticipated, society-wide crises.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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