Moral violations lead to demeaning: Non-disclosure of HIV undermines perceived psychological needs
Bibliographic record
Abstract
Dehumanization of stigmatized groups is a pressing social challenge, and to effectively address it, we must understand how it arises. Here, we identify social-cognitive antecedents of a subtle form of dehumanization known as demeaning-which occurs when a target's "uniquely human" psychological needs (e.g., for meaning in life) are downplayed relative to their physiological needs shared with other animals. We study how demeaning arises by leveraging the Agent-Deed-Consequence (ADC) framework of moral cognition, which posits that perceptions of an Agent's Deeds, and the Consequences of these Deeds, independently shape perceptions of the Agent's moral character. Because morality is fundamental to perceptions of humanity, we reasoned that the perception of (im) moral character, in turn, would impact demeaning (i.e., downplaying the Agent's psychological needs). We support this notion in a vignette experiment in a context where stigma is rampant and crucially understudied-Iran. Participants (N = 272) evaluated a stigmatized Agent-an HIV-positive individual with a history of addiction. We varied the Agent's Deed (deceiving partner vs. being honest with a partner) and its Consequence (infecting partner with disease vs not) in a 2 x 2 design. Indeed, a negative Deed and Consequence led to greater perceived immorality. Immorality, in turn, influenced perceptions of the Agent's "uniquely human" needs, but not their "lower" physiological needs shared with animals. Moreover, our Iranian participants' perceptions of what is a "uniquely human" need differ from those in previous Western samples, underscoring the need for further investigation into the sociocultural forces influencing dehumanization.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".