What Are Good‐Looking Candidates, and Can They Sway Election Results?*
Bibliographic record
Abstract
Objective In this article, we address two major gaps in the understanding of the relationship between candidate attractiveness and electoral success. With the assistance of the Victoria Police Criminal Identification Unit in Melbourne, Australia, we show how good‐looking candidates look like by building the faces of six “ideal candidates” in terms of physical attractiveness. Utilizing our “ideal candidates,” we then investigate whether candidate attractiveness can actually sway electoral results. Methods We proceed in four distinct steps, using data from the 2008 U.S. House of Representatives elections. First, we collect data on candidate attractiveness. Second, we build our “ideal candidates” and obtain their attractiveness ranking. Third, we model the effect of candidate attractiveness on candidate vote margins. Fourth, we run four hypothetical scenarios that assess whether candidate attractiveness can sway the electoral results in marginal seats. Results About two‐thirds of marginal races would trigger a different winner if the actual loser looked like our ideal candidates. In addition, virtually every single marginal race would have had a different outcome if the unsuccessful candidate looked like our “ideal candidate” and the successful candidate was very unattractive. Conclusion Candidate attractiveness can sway electoral results, provided that elections are competitive.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".