‘I’ve Just Seen a Face’: The Effects of Facial Appearance on Measures of Generalized Trust
Bibliographic record
Abstract
Research suggests various associations between generalized trust and a wide range of economic, political, and social dimensions. Despite its importance, there is considerable debate about how best to measure generalized trust. One recent solution operationalizes generalized trust as the average of trust ratings across a small set of trust domains and human faces. Here, we investigate whether heterogeneity in facial appearance affects the psychometric properties of these new instruments. In a survey experiment conducted with a sample of U.S. adults (n = 5,001), we randomly assigned respondents to one of five conditions that varied the features of human and AI-synthesized faces. Irrespective of the condition, respondents rated each face along four trust domains. We find that facial heterogeneity has negligible effects on the measurement validity and measurement equivalence of these new instruments. Small mean differences are observed for a subset of faces. These findings demonstrate the power of using faces to measure generalized trust, and the utility of using AI-synthesized faces in social science research more broadly.
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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 | 0.016 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".