Bibliographic record
Abstract
The use of the different terms “miscarriages of justice,” “wrongful convictions,” “innocence” and “exoneration” in different countries is examined. The book’s research methodologies are explained. A comparative law methodology is used to highlight similarities and differences in different jurisdictions. Many of the immediate causes, such as mistaken eyewitness identification, false confessions and false forensic evidence, are basically similar. At the same time, remedies, including what is remedied, and some structural factors, such as prejudice and discrimination, often differ. A legal process methodology is used to examine the different contributions that courts, the executive and legislatures can make to the creation, prevention and remedying of miscarriages of justice. A historical approach is used to illustrate the longstanding role of racism and prejudice and to explore whether wrongful conviction reforms are a means of legitimating unjust systems. The normative values at stake in miscarriages of justice are outlined with a focus on equality and fair trial rights, including the presumption of innocence. The issue of balancing the risks of wrongful convictions and wrongful acquittals is discussed. Finally, a detailed outline of subsequent chapters is provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.391 | 0.228 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".