Observers’ motivated sensitivity to stigmatized actors’ intent
Bibliographic record
Abstract
Does a harmful act appear more intentional-and worthy of opprobrium-if it was committed by a member of a stigmatized group? In two studies (N = 1,451), participants read scenarios in which an actor caused a homicide. We orthogonally manipulated the relative presence or absence of distal intent (a focus on the end) and proximal intent (a focus on the means) in the actor's mind. We also varied the actor's racial (Study 1) or political (Study 2) group. In both studies, participants judged the stigmatized actor more harshly than the non-stigmatized actor when the actor's level of intent was ambiguous (i.e., one form of intent was high and the other form of intent was low). These data suggest that observers apply a sliding threshold when judging an actor's intent and moral responsibility; whereas less-stigmatized actors elicit condemnation only when they cause the outcome with both types of intent in mind, more-stigmatized actors elicit condemnation when only one type, or even neither type (Study 2) of intent is in their mind. We discuss how these results enrich the literature on lay theories of intentionality.
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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 | 0.003 | 0.036 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".