Bibliographic record
Abstract
This review essay examines recent trends in American wrongful conviction scholarship. Despite calls for a ‘criminology’ of wrongful conviction, narrative about actual cases remains important. The two books reviewed explore the ambiguities and challenges of innocence work by examining cases where the authors represented wrongfully convicted persons without DNA evidence. The books both critique restrictions on post-conviction relief in the American Federal Courts. These restrictions are assessed as examples of American ‘extra-legalism’ where a very complex legal system frequently produces unjust results and fails to provide redress and accountability for injustice. The role of equality and non-discrimination norms in wrongful convictions discourse are also assessed. Valena Beety’s call for a substantive and social justice approach focused on ‘manifest justice’ and fundamental reforms to the American criminal justice system is compared to Daniel Medwed’s more narrow focus on factual innocence. It is argued that Beety’s more ambitious approach is normatively superior and more easily applied outside the distinct context of American mass imprisonment and extra-legalism.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".