Bibliographic record
Abstract
In the studies reported here, we conducted longitudinal analyses of preelection polling data to test whether an Ebola outbreak predicted voting intentions preceding the 2014 U.S. federal elections. Analyses were conducted on nationwide polls pertaining to 435 House of Representatives elections and on state-specific polls pertaining to 34 Senate elections. Analyses compared voting intentions before and after the initial Ebola outbreak and assessed correlations between Internet search activity for the term "Ebola" and voting intentions. Results revealed that (a) the psychological salience of Ebola was associated with increased intention to vote for Republican candidates and (b) this effect occurred primarily in states characterized by norms favoring Republican Party candidates (the effect did not occur in states with norms favoring Democratic Party candidates). Ancillary analyses addressed several interpretational issues. Overall, these results suggest that disease outbreaks may influence voter behavior in two psychologically distinct ways: increased inclination to vote for politically conservative candidates and increased inclination to conform to popular opinion.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".