“I’m just being honest.” When and why honesty enables help versus harm.
Bibliographic record
Abstract
Although honesty is typically conceptualized as a virtue, it often conflicts with other equally important moral values, such as avoiding interpersonal harm. In the present research, we explore when and why honesty enables helpful versus harmful behavior. Across 5 incentive-compatible experiments in the context of advice-giving and economic games, we document four central results. First, honesty enables selfish harm: people are more likely to engage in and justify selfish behavior when selfishness is associated with honesty than when it is not. Second, people are selectively honest: people are more likely to be honest when honesty is associated with selfishness than when honesty is associated with altruism. Third, these effects are more consistent with genuine, rather than motivated, preferences for honesty. Fourth, even when individuals have no selfish incentive to be honest, honesty can lead to interpersonal harm because people avoid information about how their honest behavior affects others. This research unearths new insights on the mechanisms underlying moral choice, and consequently, the contexts in which moral principles are a force of good versus a force of evil. (PsycInfo Database Record (c) 2021 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 | 0.005 | 0.032 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".