Prospective Associations Between Boys' Substance Use and Problem Behavior Histories and Their Facial Trustworthiness in Adulthood
Bibliographic record
Abstract
Introduction: People whose faces look untrustworthy tend to receive harsher social evaluations, including more severe criminal sentences. Yet little is known about how much facial trustworthiness reflects individuals' behavioral histories. We examined whether adolescent histories of delinquency and substance use predict strangers' perceptions of young men's facial trustworthiness. Methods: Boys (n = 206) recruited from schools with higher juvenile crime rates were assessed repeatedly from ages 10–24 years, including arrest records and self-reported delinquency and substance use. Coders blind to the study's purpose rated participants' facial trustworthiness from photographs taken at ages 14 and 24; parent-reported childhood family income and coder ratings of attractiveness and positive affect at age 24 were considered as controls. Results: Facial trustworthiness at age 24 (but not age 14) negatively correlated with all measures of problem behavior. Yet, self-reported tobacco use occasions from ages 12–23 had the strongest association with facial trustworthiness at age 24, a relation that persisted when controlling for arrests and delinquency from ages 12–23, other substance use, family income, ratings of age-24 positive facial affect, attractiveness, and age-14 facial trustworthiness (β = −.29, 95% CI [−.42, −.15], p < .001). Discussion: Although boys' early facial trustworthiness did not relate to their later problem behavior, men with histories of more delinquency and tobacco use appeared less facially trustworthy as adults. Appearance-related biases may have forensic and healthcare implications for young men. Additionally, prevention efforts could leverage information about the early impacts of tobacco use on appearance.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".