A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper proposes a revised version of the original Domain-Specific Risk-Taking (DOSPERT) scale developed by Weber, Blais, and Betz (2002) that is shorter and applicable to a {broader range of ages, cultures, and educational levels}. It also provides a French translation of the revised scale. Using multilevel modeling, we investigated the risk-return relationship between apparent risk taking and risk perception in 5 risk domains. The results replicate previously noted differences in reported degree of risk taking and risk perception at the mean level of analysis. The multilevel modeling shows, more interestingly, that within-participants variation in risk taking across the 5 content domains of the scale was about 7 times as large as between-participants variation. We discuss the implications of our findings in terms of the person-situation debate related to risk attitude as a stable trait.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it