Bibliographic record
Abstract
Does political affiliation of local politicians determine citizens’ compliance to social distancing behaviours? I provide causal estimates of the effect of political identity of municipal mayors on regional differences in engaging in COVID preventive behaviours in the context of Brazil. I employ a sharp regression discontinuity design based on close mayoral elections in 2016 to examine the effects of having a mayor from one of three political parties that President Jair Bolsonaro is closely associated with, by combining Facebook mobility data that tracks regional movement relative to February 2020 levels with electoral data from the 2016 mayoral municipal elections. The methodology compares municipalities that are similar along a wide array of predetermined and observable correlates of the spread of coronavirus, and where the incumbent mayor was selected as-if randomly. I find that residents of Bolsonaro-affiliated municipalities exhibit 60% smaller relative declines in regional movement and are 13% more likely to cross regional boundaries over the months of March, April, and May in 2020. The findings hold for each of the three political parties, for each of the three months since the onset of the pandemic, and after controlling for anti-lockdown measures of 15 March 2020 in Brazil.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".