Dynamics of organized violence in the wake of tropical cyclones
Bibliographic record
Abstract
Abstract Recent research highlights how the same vulnerabilities that lead to disasters also condition the impact of hazards on violent conflict. Yet it is common practice in the literature to proxy rapid-onset hazards with disaster impacts when studying political violence. This can bias upward estimates of hazard–conflict relationships and obscure heterogeneous effects, with implications for forecasting as well as disaster risk reduction and peace-building activities. To overcome this, we implement an approach that measures and models the separate components of a tropical cyclone event: the hazard, the exposure, and the impacts. We then estimate a set of models that quantify how the incidence and intensity of organized violence respond to hazard exposure. We find little evidence that the average tropical cyclone enhances or diminishes violent conflict at the country level over a two-year time horizon. Yet rather than signaling that storms do not matter for political violence, unpacking this average result reveals two countervailing effects within countries. Conflict, and especially one-sided violence against civilians, tends to escalate in regions directly exposed to the tropical cyclone. In contrast, areas outside the path of the storm may experience a decrease in conflict. These results are heterogeneous with tropical cyclone intensity, and conflict escalation is more likely to occur in settings with less effective governments. Our results underscore the importance of ex-ante efforts targeting government capacity and effective disaster risk reduction to moderate the risk of violent conflict in the wake of tropical cyclones.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".