People attribute humanness to men and women differently based on their facial appearance.
Bibliographic record
Abstract
Recognizing others' humanity is fundamental to how people think about and treat each other. People often ascribe greater humanness to groups that they socially value, but do they also systematically ascribe social value to different individuals? Here, we tested whether people (de)humanize individuals based on social traits inferred from their facial appearance, focusing on attractiveness and intelligence. Across five studies, less attractive and less intelligent-looking individuals seemed less human, but this varied by target gender: Attractiveness better predicted humanness attributions to women whereas perceived intelligence better predicted humanness attributions to men (Study 1). This difference seems to stem from gender stereotypes (preregistered Studies 2 and 3) and even extends to attributions of children's humanness (preregistered Study 4). Moreover, this gender difference leads to biases in moral treatment that confer more value to the lives of attractive women and intelligent-looking men (preregistered Study 5). These data help to explain how interpersonal judgments of individuals interact with intergroup biases to promote gender-based discrimination, providing greater nuance to the mechanisms and outcomes of dehumanization. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".