Bibliographic record
Abstract
Despite its growing economic and political importance, this is the first study in economics to investigate public opinion in the United States regarding both the allocation of government disaster aid to stricken households and communities as well as total expenditures by government on such aid. This is also the first study to bridge a gap in previous research on disasters by comparing and contrasting our results to related behavioral studies from political science, social psychology and sociology. Combining individual data from the 2006 General Social Survey with county-level information about the local environment of survey respondents, we estimate probit models to ascertain the magnitude and significance of the socioeconomic, demographic, political and experiential determinants of public opinion on these issues. Among other results, we find that Black survey respondents strongly support increasing total aid expenditures and aid to affected households and communities while income, age and a conservative political ideology largely exert a negative influence on these same variables. Surprisingly, the effects of prior experience with disasters and educational level have only a weak effect on the allocation of aid and none on the level of expenditures on aid. These and other results are consistent with only a portion of previous findings from other disciplines. Several implications of our results for current federal disaster policy are discussed and we also suggest directions for further research into this important topic.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.020 | 0.001 |
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".