Can the group disincentivize offending? Considering opt‐out thresholds and decision reversals*
Bibliographic record
Abstract
Abstract Scholars generally agree that offending decisions occur in social context, with some suggesting that choice models should explicitly integrate the notion that the deviant actions of others can incentivize offending. In this study, we investigate whether group settings can also disincentivize deviant action via reverse bandwagon effects, where individuals reverse their offending decision and express an intention to opt out of the criminal act. Based on survey data from three universities using hypothetical scenarios about theft and fighting, we find evidence of opt‐out thresholds. Our findings indicate that deviant groups can serve as both an incentive and a disincentive, and that the relationship between group size and the perceived utility of crime is more complicated than prior work has suggested. Moreover, we find that these self‐reported opt‐out thresholds vary across scenarios, indicating that socially interdependent decision‐making processes may be situation specific. In the end, the study underscores the importance of acknowledging the social context in offending decisions and highlights that group effects may be more complex and nuanced than previously discussed.
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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 | 0.029 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".