Governors and electoral hazard in the allocation of federal disaster aid
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 U.S. public disaster aid provide elected officials opportunities to engage in “electoral hazard,” where an incumbent can influence the probability of re‐election by allocating aid to influence voters' expectations of their future welfare. This is the first test for electoral hazard in the allocation of federal aid to counties exposed to the risk of economic loss from disasters by incumbent state governors running for re‐election. Using a unique county‐level data set, we estimate the determinants of the equilibrium allocation strategy of an incumbent in the presence of electoral hazard. Controlling for loss and the demographic, economic and political characteristics of at‐risk counties, we find the average incumbent governor seeking re‐election actively engages in the manipulation of voter expectations by allocating greater shares and magnitudes of the largest federal disaster aid program to those at‐risk counties that awarded the incumbent governor a plurality of votes in the preceding election.
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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 | 0.001 | 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 it