Assessing Motivations for Punishment: The Sentencing Goals Inventory
Bibliographic record
Abstract
The purpose of these studies was to develop a novel measurement, the Sentencing Goals Inventory (SGI), for understanding the underlying people’s motivations for punishing justice-involved individuals. Prior scales have focused on punishment motives such as utilitarianism (incapacitation or deterrence) and retribution (“just deserts”) but have not assessed a rehabilitation motive (punishment with the goal of addressing the cause of criminality) in tandem. Building on the previous unpublished work by Perelman and colleagues (2010), we conducted four new studies on the SGI. A slightly modified version of the scale emerged as a well-fitting model for sentencing goals. It displayed good reliability across samples, internal structure validity, and discriminant and convergent validity with other measures. This work provides a strong basis of evidence for the SGI as a measure of current social attitudes toward criminal justice sanctions and punishment that can be used in future research and to inform public policy.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".