What Are Good‐Looking Candidates, and Can They Sway Election Results?*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it