Bibliographic record
Abstract
The US presidential electoral process is remarkable for widespread spending, attention, conflict, and rhetoric. Whether the process has an immediate effect on public health has never been tested. Moreover, such a possibility rarely receives consideration when evaluating voter turnout statistics ranging around 50-60% of eligible Americans. We studied all US presidential elections for the last 32 years, beginning with Carter in 1976 and ending with Obama in 2008. For each election, we analyzed the national registry of fatal crashes in the US, along with the Tuesday immediately before and after to calculate expected numbers of individuals in fatal crashes for the nation at the time. Our main finding was that the average election leads to a 19% increase in the risk of a fatal crash during the hours of polling. This equaled about 24 people per election; was remarkably consistent across different ages and locations; and greatly exceeded the risk on New Year’s Eve, Super Bowl Sunday, or the chance of casting a pivotal vote. We conclude that efforts to mobilize the population, along with America's reliance on motor vehicles, results in increased fatal crashes during US presidential elections. We suggest more safety advocacy by electioneers who encourage people to vote. Perhaps the US president, when elected in the aftermath of fatal crashes, might also give more thought to the 100 lives lost each day from crashes in the United States.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".