Bibliographic record
Abstract
The first part of this chapter examines the prosecutorial role in wrongful convictions with special attention to guilty plea wrongful convictions. Such wrongful convictions are only recently being recognized as a problem. There is still some victim blaming of innocent accused who make rational or irrational decisions to pled guilty. Justice Rosenberg’s 2008 decision in Hanemaayer was pioneering in its recognition of the guilty plea wrongful conviction, its willingness to admit error and correct injustice and its compassionate approach to an innocent accused who pled guilty. The available evidence suggests that while prosecutors play a direct role in some wrongful convictions, they more frequently play an indirect role. The second part provides a taxonomy of strategies to employ to improve prosecutorial behavior in correcting and preventing wrongful convictions. It draws distinctions between “hard” or external strategies of regulation that involve attempts to impose sanctions and “soft” or internal strategies based on self-regulation including education, ethics and rewards. It argues that the optimal approach especially given the indirect role of prosecutors in many wrongful convictions will combine both external regulation and internal self-regulation.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".