Trust at zero acquaintance: More a matter of respect than expectation of reward.
Bibliographic record
Abstract
Trust is essential for a secure and flourishing social life, but many economic and philosophical approaches argue that rational people should never extend it, in particular to strangers they will never encounter again. Emerging data on the trust game, a laboratory economic exchange, suggests that people trust strangers excessively (i.e., far more than their tolerance for risk and cynical views of their peers should allow). What produces this puzzling "excess" of trust? We argue that people trust due to a norm mandating that they show respect for the other person's character, presuming the other person has sufficient integrity and goodwill even if they do not believe it privately. Six studies provided converging evidence that decisions to trust follow the logic of norms. Trusting others is what people think they should do, and the emotions associated with fulfilling a social duty or responsibility (e.g., guilt, anxiety) account for at least a significant proportion of the excessive trust observed. Regarding the specific norm in play, trust rates collapse when respect for the other person's character is eliminated as an issue.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".