Imagine All the People: Investigating People’s Perceptual Biases as They Pertain to Age, Race, and Gender
Bibliographic record
Abstract
Typically, perceptual biases are studied by investigating how people respond to written scenarios, without considering the mental representations people form while reading these descriptions. This paper provides a novel approach to face perception research by looking at people’s mental representations of strangers and aims to determine whether current ways of classifying people into definite race, age, and gender categories were accurate or needed to be rethought. Specifically, participants digitally reproduced the faces they imagined while reading different scenarios where strangers were described only by race, age, and gender (N = 76). Subsequently, a different set of participants rated these faces on various traits (N = 1024). In the first part of the study, participants created 9 faces from written descriptions of strangers, the last of which included information about criminal history. In the second part, participants rated these faces on dimensions of attractiveness, trustworthiness, intelligence, and physical strength for faces in the non-crime condition, and on dimensions of threat, criminality, and attractiveness for the crime condition. Linear regression models showed that age, race, and gender had various effects on scores on different dimensions, as well as on within-group variance. For instance, older faces were awarded lower attractiveness ratings than younger faces overall, an effect which was also moderated by race, with older age being less predictive of attractiveness ratings for Black faces. Furthermore, there was significantly less variability in attractiveness ratings for Black faces than White faces. Overall, this study revealed that stereotypes do not always adhere to clear-cut categories of race, age, and gender, suggesting that they may be applied somewhat dimensionally rather than categorically.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".