Racial Resentment, Hurricane Sandy, and the Spillover of Racial Attitudes into Evaluations of Government Organizations
Bibliographic record
Abstract
Abstract This study explores the relationship between individuals’ racial attitudes, exposure to information cuing them to think about President Obama, and evaluations of the government's response to Hurricane Sandy. Using a split ballot experiment embedded in a large internet panel fielded during the 2012 presidential election, we show that respondents’ evaluations of President Obama's response to Hurricane Sandy were based on their racial attitudes. We next examined the possibility for racial attitudes to “spill over” into how people evaluate governmental institutions and organizations associated with President Obama. We found evidence that respondents who were cued to think about President Obama and were impacted by Hurricane Sandy were more likely to base their evaluations of the Federal Emergency Management Agency's response to the disaster on their racial attitudes. In short, linking President Obama to Hurricane Sandy led people to ground their evaluations of an organization tasked with coordinating the response to Hurricane Sandy in their racial attitudes. Our research suggests that racial attitudes are important predictors of how individuals perceive President Obama's effectiveness as well as the efficacy of related government organizations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".