Blinded by Beauty? Physical Attractiveness and Candidate Selection in the U.S. House of Representatives
Bibliographic record
Abstract
Objective In this article we show that physical attractiveness matters as a heuristic device for uninformed voters but not for politically savvy voters. Methods Drawing on a two‐step experiment, we first ask over 100 students to rank the physical attractiveness of candidates to the U.S. House of Representatives. Second, we create a treatment and a control group comprising each of 1,200 research different subjects. We ask the first group to indicate their vote choice by merely looking at the picture of candidates for the 2008 U.S. House of Representatives elections, while the second group has a picture and a detailed description of the political/professional competence of the contenders at their disposal. Results We find that our first group of study subjects representing all those voters who are politically uninformed tend to cast their ballot for the better‐looking candidate, whereas the second group, representing politically knowledgeable individuals, choose the more competent candidate. Conclusion Our experimental study provides evidence that uninformed or politically unknowledgeable voters use political appearance as a heuristic device in casing their ballot at elections.
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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 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".