The Rest of the Story of R. v. Stinchcombe: A Case Study in Disclosure Issues
Bibliographic record
Abstract
Stinchcombe is a decision which is oft-quoted but not well understood or properly interpreted by the courts. This article reviews the facts of the Stinchcombe litigation and suggests that the Courts have failed to properly apply the principles enunciated therein. In the initial Supreme Court decision Sopinka J. suggested the adoption of the civil model of pretrial disclosure in the criminal context: the Crown, he suggested, should err on the side of inclusion and consider it a constitutional duty to disclose everything that could be relevant to the case. The courts have failed to properly apply this decision, as evidenced by the judicial interpretation of "relevance," the expectation that defence counsel will police prosecutors' compliance with the disclosure obligation, and the failure of the courts to apply appropriate remedies or sanctions where disclosure obligations have not been properly met. The author suggests that practitioners should reconsider the principles of Stinchcombe, especially in light of proposed changes to the Alberta Rules of Court which might make disclosure requirements in the civil process less stringent.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".