The CCRC as an Option for Canada: Forwards or Backwards?
Bibliographic record
Abstract
It may appear to strike a discordant note in a book about ‘critical perspectives’ even to posit that the Criminal Cases Review Commission (CCRC) could perform as any kind of role model. However, the CCRC has long vaunted its attractiveness as an original and effective device. Though no longer unique, following the establishment of the Norwegian Criminal Cases Review Commission (NCCRC) (NCCRC, 2008), it has attracted interest from jurisdictions as diverse as Holland and Japan (CCRC, 2006a). As might be expected, common law jurisdictions have also cast curious glances. In particular, Canadian interest has been expressed on several occasions, most recently through the work of the Inquiry into Pediatric Forensic Pathology under Justice Stephen T. Goudge in Ontario, which has embarked upon a systemic examination of the lessons to be learnt from other jurisdictions (Goudge, 2007).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".