Bibliographic record
Abstract
Concerns about the role of prejudice and racial discrimination first expressed by Voltaire and Zola were often at the forefront of pre-DNA campaigns to correct wrongful convictions. Despite this, the American innocence movement frequently neglected the role of racism in wrongful convictions. It neglected links between lynching and frequent DNA exonerations, where white victims misidentified Black men. Racism was recognized in the wrongful convictions of the Exonerated (Central Park) Five but not in other similar wrongful convictions of Black teenagers. Trump mobilized anti-Black racism in his calls for the Five to be executed. The role of both anti-Indigenous and anti-Black racism in the 1971 wrongful conviction of Donald Marshall Jr. for the murder of a Black teenager in Canada is examined. A 1989 public inquiry into this wrongful conviction did not ignore racism in the same way as similar American inquiries into wrongful convictions. Patterns of anti-Indigenous racism and the role of stereotypes in the wrongful conviction of Indigenous men in Australia, Canada, New Zealand and the United States are identified. Finally, the place of anti-racism in the future evolution of innocence movements is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".