Getting Mean, Getting Even, Getting Justice: Punishment and a Search for Alternatives
Bibliographic record
Abstract
When someone is hurt or wronged, a common response is to strike back. It occurs at both the individual and societal levels. Hurts are to be punished, but not unduly so. Fairness and justice also apply. In almost all societies, punishment is a consequence of breaking the law, and the application of punishment is highly regulated. There are many purposes for punishment within the criminal justice system, which include retribution, denunciation of the act, and deterrence. This chapter focuses on these varying purposes. The psychology of punishment shows that punishment only “works” under very specific conditions, conditions that the criminal justice system cannot replicate. Laboratory studies of punishment clearly show that for punishment to be effective it must follow the behavior with certainty and immediacy and at the right intensity. In the real world, laboratory conditions are impossible. Punishment has many undesirable “side effects” that are counterproductive in the suppression of antisocial behavior. Restorative justice, with the inclusion of appropriate treatment, offers a viable alternative to “get tough” approaches in reducing crime.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".