Nonverbal facial cues signaling sexually transmitted infections cause dehumanization and discrimination
Bibliographic record
Abstract
Dehumanization often underlies the social ostracism, exclusion, and discrimination experienced by stigmatized group members. Given findings that people can detect sexually transmitted infection (STI) status from nonverbal facial cues, we tested whether people would dehumanize and discriminate against STI-positive individuals from detecting their stigmatized status. Specifically, we hypothesized that nonverbal stigma cues would stimulate dehumanizing reactions that lead to biases against hiring STI-positive individuals. Results showed that people dehumanize STI-positive individuals based on their nonverbal stigma cues (i.e., negative affect; Study 1), except when STI status is explicitly disclosed (Study 2), which leads to potential hiring biases (Study 3). Dehumanization and discrimination against STI-positive individuals may therefore depend on the stigma's legibility from nonverbal cues but may be tempered by explicit information about STI status.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".