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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".