Morally questionable actors’ meta-perceptions are accurate but overly positive
Bibliographic record
Abstract
We examine how actors think others perceive their morally questionable behavior (moral meta-perception) across a diverse set of real-world moral violations. Utilizing a novel methodology, we solicit written instances of actors’ morally questionable behavior (N_total=135), measure motives and meta-perceptions, then provide these accounts to separate samples of third-party observers (N_total=933), using US convenience and representative samples (N_actor-observer pairs=4,615). We find that morally questionable actors can accurately predict how they are perceived, how they are uniquely perceived relative to the average morally questionable actor, and how they are misperceived. Actors who are better at judging the motives of other morally questionable actors also have more accurate meta-perceptions. Yet accuracy is accompanied by two distinct biases: overestimating the positive perceptions others’ hold, and believing one’s motives are more clearly perceived than they are. These results contribute to a detailed account of the multiple components underlying both accuracy and bias in moral meta-perception.
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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 | 0.006 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".