Redefining Justice: Explainning Why Treating Offenders Better Than They Deserve Enhances Social Welfare and Moral Progress
Bibliographic record
Abstract
This study calls into question the long-standing retributive justice framework, positing that contemporary societies ought to place greater emphasis on treating offenders more favorably than their transgressions might “warrant.” The core aim of this approach is to promote rehabilitation, cut down on recidivism, and make the most of social resources. By drawing on ethical theories, real-world applications of restorative justice, and cost-benefit assessments, the research explores how moving away from punitive measures toward rehabilitative strategies aligns with the changing standards of human dignity, enhances efforts to heal communities, and delivers lasting social advantages. Using case examples from countries such as Norway, the United Kingdom, and Canada, among others, the study shows that “improved treatment”—which includes access to education, integration into community life, and restorative conversation—redefines justice as a means of mending social harm, rather than just a way to exact revenge. In the end, the research argues that this shift is a sign of a mature civilization acknowledging the complexity of human nature and the possibility of individuals turning their lives around.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".