The Good-looking Giver Effect: The Relationship Between Doing Good and Looking Good
Bibliographic record
Abstract
Evidence exists that beautiful is seen as good: the halo effect wherein more physically attractive people are perceived to be good, and the reverse halo that good is seen as beautiful. Yet research has rarely examined the evidence linking the beautiful with the good, or the reverse, without the halo effect. We examine the relationship between physical attractiveness (beauty) and giving behaviors (good), where ratings of attractiveness are independent of giving behaviors. We use three U.S. datasets: (a) a nationally representative sample of older adults (NSHAP), (b) a nationally representative longitudinal study of adolescents (ADD Health), and (c) the 54-year Wisconsin Longitudinal Study (WLS), to present evidence that these two characteristics (attractiveness and giving) are indeed correlated without the halo effect. We find a ‘good-looking giver’ effect–that more physically attractive people are more likely to engage in giving behaviors, and vice versa. Thus, in ecologically valid real-world samples, people who do good are also likely to look good.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".