What is the moral person like? An examination of the shared and unique perspectives on moral character
Bibliographic record
Abstract
OBJECTIVE: The psychological profile of the moral person might depend on whose perspective is being used. Here, we decompose moral impressions into three components: (a) Shared Moral Character (shared variance across self- and informant reports), (b) Moral Identity (how a person uniquely views their morality), and (c) Moral Reputation (how others uniquely view that person's morality). METHOD: In two samples (total N = 458), we used an extended version of the Trait-Reputation-Identity model to examine the extent to which each perspective accounts for the overall variance in moral impressions and the degree to which social and personal outcomes were associated with each perspective, controlling for method variance (i.e., positivity and acquiescence bias). RESULTS: Results suggest that moral character impressions are strongly influenced by positivity and largely idiosyncratic. All components were related to higher levels of agreeableness. For the most part, however, the three components had unique correlates: people higher in Shared Moral Character tended to have higher standings on conscientiousness and honesty-humility, were more respected, and donated more during an in-lab game; people higher in Moral Identity endorsed various moral foundations to a greater extent; and people higher in Moral Reputation valued the loyalty foundation less. CONCLUSION: These results demonstrate the value of considering multiple perspectives when measuring moral character.
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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 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".