Who is trustworthy? Predicting trustworthy intentions and behavior.
Bibliographic record
Abstract
In this investigation, we deepen our understanding of trustworthiness. Across six studies using economic games that measure trustworthy behavior and survey items that measure trustworthy intentions, we explore the personality traits that predict trustworthiness. We demonstrate that guilt-proneness predicts trustworthiness better than a variety of other personality measures, and we identify sense of interpersonal responsibility as the underlying mechanism by both measuring it and manipulating it directly. People who are high in guilt-proneness are more likely to be trustworthy than are individuals who are low in guilt-proneness, but they are not universally more generous. We demonstrate that people high in guilt-proneness are more likely to behave in interpersonally sensitive ways when they are more responsible for others' outcomes. We also explore potential interventions to increase trustworthiness. Our findings fill a significant gap in the trust literature by building a foundation for investigating trustworthiness, by identifying a trait predictor of trustworthy intentions and behavior, and by providing practical advice for deciding in whom we should place our trust. (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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".