Distorting the Prosecution Process: Informers, Mandatory Minimum Sentences, and Wrongful Convictions
Bibliographic record
Abstract
As the use of mandatory minimum sentences becomes more common in Canada, it is important to consider a range of potential consequences that are neither intended nor anticipated. This article considers the implications of mandatory minimum sentences in contributing to wrongful convictions. It considers the impact of these sentences on two significant processes in the criminal justice system, plea bargaining and the development of informers, and argues that both processes are vulnerable to distortions. These distortions, which include the wrongful conviction of innocent people, can be exacerbated by the threat of mandatory minimum prison sentences. In the case of plea bargaining, innocent people may plead guilty to lesser offences to avoid mandatory minima. In regard to the development of informers, the article concentrates primarily on the role of mandatory minimum sentences in the matter of “jailhouse informer” witnesses who have been associated with a significant number of wrongful convictions. Experience in the United States, where these sentences are most widely used, informs the analysis which is nonetheless focused on the Canadian criminal justice system.
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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 | 0.015 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 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".