Bibliographic record
Abstract
Causes, Responses, Remedies Innocent people are regularly convicted of crimes they did not commit.A number of systemic factors have been found to contribute to wrongful convictions, including eyewitness misidentification, false confessions, informant testimony, official misconduct, and faulty forensic evidence.In Miscarriages of Justice in Canada, Kathryn M. Campbell offers an extensive overview of wrongful convictions, bringing together current sociological, criminological, and legal research, as well as current case-law examples.For the first time, information on all known and suspected cases of wrongful conviction in Canada is included and interspersed with discussions of how wrongful convictions happen, how existing remedies to rectify them are inadequate, and how those who have been victimized by these errors are rarely compensated.Campbell reveals that the causes of wrongful convictions are, in fact, avoidable, and that those in the criminal justice system must exercise greater vigilance and openness to the possibility of error if the problem of wrongful conviction is to be resolved.kathryn m. campbell is an associate professor in the Department of Criminology at the University of Ottawa.She is also the faculty director of Innocence Ottawa, a pro-bono, student-run innocence project that assists individuals who have been wrongly convicted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.666 | 0.509 |
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".