Perceived mental illness is associated with judgments of less agency, yet more moral wrongness
Bibliographic record
Abstract
For a set of 10 conditions (e.g. homosexuality, obesity, drug addiction), we explored associations between moral judgments, agency evaluations, and perceptions that a condition is a mental illness. In a preregistered study ( n = 1,249 U.S. adults), we found that perceptions of lower agency were associated with decreased moral wrongness judgments, as well as increased perceptions of mental illness, yet perceived moral wrongness was the most robust predictor of perceived mental illness. In other words, although perceived mental illness was associated with evaluations that tend to be morally exonerating (such as less control and greater difficulty changing), we observed positive associations between wrongness judgments and perceived mental illness. We also found that—at least within our set of conditions—political conservatives tended to evaluate conditions as more controllable, more morally wrong, and more of a mental illness, yet on the whole, ideology was not a reliable predictor of perceived mental illness. Instead, liberals and conservatives with similar wrongness evaluations tended to similarly ascribe mental illness. These findings raise questions about potential causal relationships between mental illness perceptions and moral evaluations and the possibility that perceived moral wrongness might sometimes contribute to perceptions that a condition ought to be considered a mental illness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".