The Collateral Consequence Conundrum: Comparative Genealogy, Current Trends, and Future Scenarios
Bibliographic record
Abstract
Abstract Collateral consequences (CCs) of criminal convictions such as disenfranchisement, occupational restrictions, exclusions from public housing, and loss of welfare benefits represent one of the salient yet hidden features of the contemporary American penal state. This chapter explores, from a comparative and historical perspective, the rise of the many indirect “regulatory” sanctions flowing from a conviction and discusses some of the unique challenges they pose for legal and policy reform. US jurisprudence and policies are contrasted with the more stringent approach adopted by European legal systems and the European Court of Human Rights (ECtHR) in safeguarding the often blurred line between criminal punishments and formally civil sanctions. The aim of this chapter is twofold: (1) to contribute to a better understanding of the overreliance of the US criminal justice systems on CCs as a device of social exclusion and control, and (2) to put forward constructive and viable reform proposals aimed at reinventing the role and operation of collateral restrictions flowing from criminal convictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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 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".