Beyond Self-Reported Scales and Psychometric Tests: Virtual Reality as a Tool for Measuring Self-Control and Predicting Victimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study uses immersive Virtual Reality to introduce a new behavioral measure of self-control and compares its predictive validity for victimization with traditional psychometric assessments. Self-control, a key construct in the Self-Control Theory, is often linked to victimization risk. However, conventional self-reported scales and psychometric tools struggle to accurately capture real-world manifestations of this dynamic trait. These limitations hindered the application of the theory in developing prevention programs. VR technology offers a unique solution by simulating plausible, controlled risk scenarios, enabling direct observation of participants’ decision-making. A sample of 160 participants completed the Balloon Analogue Risk Task (BART) and navigated a VR urban environment, choosing between well-maintained and disorderly paths. Behavioral indicators of self-control were derived from their choices in the VR scenario. VR-based measure of risk avoidance was a stronger predictor of physical victimization than the BART, highlighting its potential to capture nuanced decision-making processes linked to environmental risk cues. These findings support the value of VR in criminological research and its potential applications in prevention programs. By providing actionable insights into risk-related behaviors, VR-based methods can bridge theoretical constructs and practical interventions, particularly in reducing victimization risks in urban environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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