The Regulatory Offence Revolution in Criminal Justice: The Choice Architecture of Regulatory Offences
Bibliographic record
Abstract
Courts, scholars, and lawyers tend to overlook one of the most salient features that differentiate crimes and regulatory offences: choice architecture. The concept of “choice architecture” refers to how the presentation of options shapes decision-making. This article argues that crimes and regulatory offences employ different forms of choice architecture in the criminal justice process. It advances three core arguments. First, in the charging and plea phase, regulatory prosecutions nudge defendants to plead guilty by default, while criminal prosecutions automatically enrol defendants into non-guilty pleas. Second, when assessing culpability (or moral fault), regulatory prosecutions incorporate inculpatory default rules that presume guilt and foster efficiency. In contrast, criminal prosecutions incorporate exculpatory default rules that presume innocence and aim to prevent wrongful convictions. Third, in the context of sentencing, impecunious defendants who are charged with a regulatory offence must often opt in to receive a proportionate sanction. Outside of mandatory minimum sentencing contexts, defendants who are charged with a crime enrol into a sentencing scheme that considers proportionality constraints by default. Ultimately, this article deepens our understanding of the different choice architecture that governs crimes and regulatory offences, and lays the foundation for future scholarship that explores the criminal justice system’s choice architecture more generally.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".