Bad boys and mean girls: Judging aggressive potential in child faces
Bibliographic record
Abstract
Facial width-to-height ratio (WHR) is a sexually dimorphic trait that is correlated with aggressive behavior in men and with adult and child observers’ judgments of aggression in male faces (Short et al., 2011). The sexual dimorphism emerges at puberty, coincident with rises in testosterone (Weston et al., 2007). No correlation exists between WHR and aggressive behavior in women, but observers perceive women with higher WHR as aggressive, although the correlation is weaker for female (r=.40) than male (r=.70) faces (Geniole et al., submitted). We examined whether 9-year-old children’s WHR is correlated with aggressive behavior and whether observers’ estimates of aggression are correlated with children’s WHR. Nine-year-olds played a computer game that measures aggression and were photographed (data to date, n=14). They then rated adult male, and male and female child faces on how aggressively each person would play the game. A group of adults (n=24) rated the same faces. There was no correlation between aggressive behavior and children’s WHR (r=.076, p>.50). Across faces, the correlation between estimates of aggression and WHR were significant for adult faces (rs=.68 and .70 for adults and children observers respectively, ps<.01) but not child faces (rs=.29 and .21, ps>.05). A 2 (participant age) x 2 (face age) ANOVA indicated that individuals’ correlations between estimates of aggression and WHR were higher for adult faces (r=.40 and .30 for adults and children observers respectively) than child faces (r=.16 and .12), p<.001, although single sample t-tests showed that all four correlations were significant, ps<.05. The main effect of participant age and the participant age x face age interaction were not significant, ps>.50. Our results show that observers do not overgeneralize trait perception in adult male faces to child faces and suggest that the correlation between WHR and aggressive behavior in men cannot be attributed to self-fulfilling prophecy. Meeting abstract presented at VSS 2012
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".