Nudging the better angels of our nature: A field experiment on morality and well-being.
Bibliographic record
Abstract
A field experiment examines how moral behavior, moral thoughts, and self-benefiting behavior affect daily well-being. Using experience sampling technology, we randomly grouped participants over 10 days to either behave morally, have moral thoughts, or do something positive for themselves. Participants received treatment-specific instructions in the morning of 5 days and no instructions on the other 5 control days. At each day's end, participants completed measures that examined, among others, subjective well-being, self-perceived morality and empathy, and social isolation and closeness. Full analyses found limited evidence for treatment- versus control-day differences. However, restricting analyses to occasions on which participants complied with instructions revealed treatment- versus control-day main effects on all measures, while showing that self-perceived morality and empathy toward others particularly increased in the moral deeds and moral thoughts group. These findings suggest that moral behavior, moral thoughts, and self-benefiting behavior are all effective means of boosting well-being, but only moral deeds and, perhaps surprisingly, also moral thoughts strengthen the moral self-concept and empathy. Results from an additional study assessing laypeople's predictions suggest that people do not fully intuit this pattern of results. (PsycInfo Database Record (c) 2020 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".