The Reasons We Punish: Creating and Validating a Measure of Utilitarian and Retributive Punishment Orientation
Bibliographic record
Abstract
Previous researchers have investigated the situational use of punishment, but the overall reasons why people punish have received less attention.The aim of this study was to create a measure of individual differences in punishment orientation.200 participants completed a 30-item questionnaire designed to measure retributive and utilitarian punishment orientation.Exploratory factor analysis uncovered a 'pro-punishment' factor, as well as 'ideal retributive' and 'ideal utilitarian' factors.An additional sample of 200 participants completed a revised version of the scale; confirmatory factor analysis yielded acceptable model fit for harsh utilitarian, harsh retributive, and ideal retributive dimensions.The scale showed poor divergent validity, with the factors having moderate relationships with attitudes toward the legal system (Schiffhauer & Wrightsman, 1995).Predictive validity assessments showed that participants favoured one orientation dependent on the context, resulting in poor predictive utility.This scale may nonetheless contribute to a better understanding of lay punishment ethics.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".