Retribution, Restoration, and White-Collar Crime
Bibliographic record
Abstract
A "restorative" approach to criminality and conflict has been proposed in a number of common law jurisdictions in a variety of legal contexts, both civil and criminal, with an interesting exception: white-collar crime, which is discussedin an almost exclusively retributive vocabulary. This paper explores what a specifically restorative response to white-collar crime might look like, a response which above all else would seek to heal the harm the crime has done. In particular,the author looks at the possibilities for voluntary participation of victims and offenders; broad stakeholder inclusion and a focus on future relations rather than past offences-all necessaryparts of a restorative encounter The author concludes that white-collar crime, by its nature, lends itselfpoorly to restoration. Corporate crime, howeverthat in which the corporation rather than the individual can be identified as an offender -presents a much better fit.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".