Hypocritical flip-flop, or courageous evolution? When leaders change their moral minds.
Bibliographic record
Abstract
How do audiences react to leaders who change their opinion after taking moral stances? We propose that people believe moral stances are stronger commitments, compared with pragmatic stances; we therefore explore whether and when audiences believe those commitments can be broken. We find that audiences believe moral commitments should not be broken, and thus that they deride as hypocritical leaders who claim a moral commitment and later change their views. Moreover, they view them as less effective and less worthy of support. Although participants found a moral mind changer especially hypocritical when they disagreed with the new view, the effect persisted even among participants who fully endorsed the new view. We draw these conclusions from analyses and meta-analyses of 15 studies (total N = 5,552), using recent statistical advances to verify the robustness of our findings. In several of our studies, we also test for various possible moderators of these effects; overall we find only 1 promising finding: some evidence that 2 specific justifications for moral mind changes-citing a personally transformative experience, or blaming external circumstances rather than acknowledging opinion change-help moral leaders appear more courageous, but no less hypocritical. Together, our findings demonstrate a lay belief that moral views should be stable over time; they also suggest a downside for leaders in using moral framings. (PsycINFO Database Record
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".