The Role of Innocence Commissions: Error Discovery, Systemic Reform or Both?
Bibliographic record
Abstract
This article examines the role of innocence commissions as emerging criminal justice institutions. It draws a distinction between commissions devoted to the correction of errors in individual cases and commissions which make systemic reform recommendations in an effort to prevent wrongful convictions in future cases. The British and Scottish Criminal Cases Review Commission and the North Carolina Innocence Inquiry Commission are examined as examples of the former type of commission while Canadian public inquiries and commissions in Illinois, California and Virginia are examined as examples of the latter type of commission. Innocence commissions have had difficulties combining error correction and systemic reform because there are differences and tensions between the two functions. Error correction commissions play a quasi-judicial role while systemic reform commissions often engage in political compromise and advocacy. Although there is a need for both error correction and systemic reform with regard to wrongful convictions, more attention needs to be paid to the precise objectives and limitations of innocence commissions as new and fragile criminal justice institutions.
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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 | 0.075 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".