Miscarriages of Justice, Wrongful Convictions and Proven Innocence as Means of Rationing Justice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter defines the different terms “miscarriage of justice,” “wrongful convictions” and “proven innocence.” Although these terms are often used interchangeably with differences ascribed to customs and semantics, there are critical differences between them. Miscarriages of justice is the broadest term. In some definitions, it can include any violation of rights. In the criminal context, miscarriages of justice can include unfair trials and unwarranted pre-trial detentions. A wrongful conviction is a narrower term that requires a conviction that is subsequently overturned. As measured in recently developed registries, wrongful convictions are convictions overturned on the basis of new evidence relevant to guilt or innocence. Finally, the narrowest term is proven innocence. This approach is most popular in the United States, where it is also called factual or actual innocence. It was pioneered by Edwin Borchard and used by innocence projects. Formalistic arguments that proven innocence does not violate the presumption of innocence are critiqued. Consistent with Guido Calabresi’s and Phillip Bobbitt’s tragic choice theory, the use of the different terms differs over time and place, and they are used to ration justice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it