Weather Effects on Social Movements: Evidence from Washington, D.C., and New York City, 1960–95
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 Scholars have been taking the impact of weather on social movements for granted for some time, despite a lack of supporting empirical evidence. This paper takes the topic more seriously, analyzing more than 7000 social movement events and 36 years of weather records in Washington, D.C., and New York City (1960–95). Here, “good weather” is defined as midrange temperature and little to no precipitation. This paper uses negative binomial regression models to predict the number of social movements per day and finds social movements are more likely to happen on good days than bad, with seasonal patterns controlled for. Results from logistic regression models indicate violence occurs more frequently at social movement events when it is warmer. Most interestingly, the effect of weather is more salient when there are more political opportunities and resources available. This paper discusses the implications and suggests future research on weather and social movement studies.
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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 | 0.000 | 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) | 0.001 | 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