Is It All About Retribution? The Flexibility of Punishment Goals
Bibliographic record
Abstract
Abstract Current literature suggests that laypeople’s punishment is primarily driven by retributive reasons (i.e., to give offender their just deserts) rather than utilitarian purposes such as special prevention (i.e., to prevent recidivism of the offender) or general prevention (i.e., to prevent the imitation of the crime by others). One explanation for this may be that individuals tend to focus on salient cues while ignoring others when making a decision and critically, generally pay relatively little attention to secondary or long-term effects of their decision-making. This suggests that people’s punishment goals may be subject to the information salient about the crime situation. Specifically, individuals may only pursue utilitarian goals with their punishment, if aspects related to such long-term consequences of punishment are salient (such as information about the offender or the broad circumstances surrounding the crime). To examine this, we manipulated the salience of different aspects in a scenario describing a crime. In two preregistered experiments, participants were asked to choose from (Experiment 1, N = 291) or rate the appropriateness of (Experiment 2, N = 366) different reactions to the crime; these reactions were pretested for the degree to which they served each of the punishment goals: retribution, special prevention, and general prevention. As hypothesized, we found that participants’ punishment goals were associated with the salience of specific aspects of the scenario describing the crime situation. This extends on research suggesting that laypeople’s punishment goals are malleable and may depend on the research design employed by a particular study.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".