Handsome Devil: Functional Projection and the Physical Attractiveness Stereotype
Bibliographic record
Abstract
The physical attractiveness stereotype is a well-documented phenomenon wherein attractive people are ascribed more positive traits (e.g., intelligence, social competence, trustworthiness) than their less attractive counterparts (Eagly et al., 1991; Eckel & Wilson, 2006; Klebl et al., 2021). These findings can be readily explained as a kind of halo effect. However, the usual “beauty is good” effect might plausibly disappear—or even reverse-- depending on the sex of the perceiver relative to the target. Whereas most people may typically respond positively to highly attractive opposite-sex targets, highly attractive same-sex targets may be viewed less positively, and perhaps even negatively, compared to less attractive same-sex targets. This is because a highly attractive same-sex target may represent a particular kind of threat: a rival in the mating game (Buss et al., 2000)—for example: someone who might not only be motivated to move in on someone else’s mate and (because they are highly attractive) might actually be capable of luring that mate away. In other threat-relevant contexts, perceivers ascribe threat-connoting characteristics to people who are perceived to pose a threat (Haselton & Nettle, 2006; Neuberg, Kenrick, & Schaller, 2011). An analogous outcome may occur in this context. If so, then highly attractive same-sex targets wouldn’t simply be ascribed positive traits. They would instead be judged to have a specific set of characteristics that, in combination, connote their motivation and ability to be a threat in the mating game (e.g., a mate-poacher). This could manifest not only in judgments about their personality traits, but also in judgments about their tendencies to experience specific emotions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".