I can take the risk, but you should be safe: Self-other differences in situations involving physical safety
Bibliographic record
Abstract
Abstract Prior research on self-other differences involving risk have found that individuals make riskier decisions for others than for the self in situations where risk taking is valued. We expand this research by examining whether the direction of self-other differences reverses when risk aversion is valued, as predicted by social values theory (Stone & Allgaier, 2008). Two studies tested for self-other differences in physical safety scenarios, a domain where risk aversion is valued. In Study 1, participants read physical safety and romantic relationship scenarios and selected what they would decide for themselves, what they would decide for a friend, or what they would predict their friend would decide. In Study 2, participants read public health scenarios and either decided or predicted for themselves and for a friend. In keeping with social values theory, participants made more risk-averse decisions for others than for themselves in situations where risk aversion is valued (physical safety scenarios) but more risk-taking decisions for others than for themselves in situations where risk taking is valued (relationship scenarios). Further, we show that these self-other differences in decision making do not arise from incorrectly predicting others’ behaviors, as participants predicted that others’ decisions regarding physical safety scenarios would be either similar (Experiment 1) or more risk taking (Experiment 2) than their own decisions.
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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 | 0.002 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".