When the ones we love misbehave: Exploring moral processes within intimate bonds.
Bibliographic record
Abstract
How do we react when our romantic partners, friends, or family members behave unethically? When close others misbehave, it generates a powerful conflict between observers' moral values and their cherished relationships. Previous research has almost exclusively studied moral perception in a social vacuum by investigating responses to the transgressions of strangers; therefore, little is known about how these responses unfold in the context of intimate bonds. Here we systematically examine the impact of having a close relationship with a transgressor on perceptions of that transgressor, the relationship, and the self. We predicted less negative emotional and evaluative responses to transgressors and smaller consequences for the relationship, yet more negative emotional and evaluative responses to the self when close others, compared with strangers or acquaintances, transgress. Participants read hypothetical wrongdoings (Study 1), recalled unethical events (Study 2), reported daily transgressions (Study 3; preregistered), and learned of novel immoral behavior (Study 4) committed by close others or comparison groups. Participants reported less other-critical emotions, more lenient moral evaluations, a reduced desire to punish/criticize, and a smaller impact on the relationship (compared with acquaintances) when close others versus strangers or acquaintances transgressed. Simultaneously, participants reported more self-conscious emotions and showed some evidence of harsher moral self-evaluations when close others transgressed. Underlying mechanisms of this process were examined. Our findings demonstrate the deep ambivalence in reacting to close others' unethical behaviors, revealing a surprising irony-in protecting close others, the self may bear some of the burden of their misbehavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".