Being “good” or “good enough”: Prosocial risk and the structure of moral self-regard.
Bibliographic record
Abstract
This model predicts that people are primarily concerned with whether their prosocial behavior legitimates the claim that they have acted morally, a claim that often diverges from whether their behavior is in the best interests of the recipient. Specifically, it predicts that for people to feel moral following a prosocial decision, that decision need not have promised the greatest benefit for the recipient but only one larger than at least one other available outcome. Moreover, this model predicts that once people produce a benefit that exceeds this threshold, their moral self-regard is relatively insensitive to the magnitude of benefit that they produce. In 6 studies, we test this moral threshold model by examining people's prosocial risk decisions. We find that, compared with risky egoistic decisions, people systematically avoid making risky prosocial decisions that carry the possibility of producing the worst possible outcome in a choice set-even when this means avoiding a decision that is objectively superior. We further find that this aversion to producing the worst possible prosocial outcome leads people's prosocial (vs. egoistic) risk decisions to be less sensitive to those decisions' maximum possible benefit. We highlight theoretical and practical implications of these findings, including the detrimental consequence that people's desire to protect their moral self-regard can have on the amount of good that they produce. (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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".