Effects of Moral Claims on Charitable Support for the Stigmatized
Bibliographic record
Abstract
In this research, we examine the extent to which charitable support for the stigmatized can be enhanced by a moral claim, which occurs when a stigmatized person is attributed an unselfish (versus self-interested) motive for a socially desirable behavior. We find that prospective donors infer a more positive morality when a moral claim is made about a stigmatized person in need, which leads to a greater willingness to provide them with charitable support. The effect does not occur when the same moral claim is made for a person in equivalent need who is accepted or honored in society. We find that moral claims are effective when based on unsubstantiated statements about a mentally ill person’s morality (Study 1), when a drug thief volunteers to help flood victims, rather than helps them as part of her employment (Study 2), and when a homeless criminal returns a lost wallet to do the right thing, rather than to receive a reward (Study 3). The research contributes to our understanding of the role of moral judgments that lead to social rejection and provides insights into how the negative stereotypes that affect the stigmatized can be changed.
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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 | 0.004 | 0.029 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".