Latent Structure of Risk Perception
Bibliographic record
Abstract
Abstract Risk-taking behavior affects many aspects of life, including maladaptive behaviors such as illicit substance use, unsafe driving, and risky sexual behavior. Risk-taking has been measured using both self-report measures and behavioral tasks designed for the purpose, but there is little consensus in the associations among measures and our understanding of the latent constructs underlying different forms of risk is limited. In the present study we examined the construct of risk using data from over 1000 young adults who completed measures of risk-taking, including self-reports of perception of risk, propensity to engage in risky behaviors and performance on behavioral tasks designed to measure risk. To examine the latent structure of risk preferences, we conducted a principal component analysis (PCA). The PCA revealed a latent structure of three distinct components of risk-taking behavior: “ Lifestyle Risk Sensitivit y”, “ Financial Risk Sensitivity” , and “ Behavioral Risk Sensitivity”, which consisted only of the Balloon Analogue Risk Task (BART; Lejuez et al., 2002). As expected, risk-taking and perception of risk differed in men and women. Yet, the PCA components were similar in men and women. Future work utilizing additional measures of risk-taking behavior in more heterogeneous samples will help to identify the true biobehavioral constructs underlying these behaviors.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".