Evidence: A Canadian Casebook, 3rd Edition
Bibliographic record
Abstract
Designed to meet the needs of second-year and third-year courses in evidence, the third edition of Evidence: A Canadian Casebook investigates the rules and principles that govern how facts are established in legal proceedings. The author team, consisting of well-respected scholars from a number of Canadian law schools, has developed a casebook that sets itself apart from other resources by weaving a single case study — inspired by an actual murder prosecution — throughout the entire text. At the end of each chapter, the authors introduce new developments in the case study, and students are asked to apply what they have just learned to solve problems that emerge in the course of that case. As students work through this unique text, they will become familiar with the case study and invested in solving the evidence problems it poses. Additionally, the third edition of Evidence: A Canadian Casebook will help instructors bridge the gap between evidence law in theory and in practice, giving students a deeper sense of the methodology that informs all evidence analysis.\nThe third edition reflects new developments in evidence law in Canada, including the Supreme Court of Canada’s latest decision on the principled approach to hearsay, the streamlining of the treatment of confessions and improperly obtained evidence, and the Supreme Court of Canada’s reinterpretation of section 24(2) of the Charter. Other cases added are F.H. v. McDougall, (2008), in which the Supreme Court addresses credibility and the civil standard of proof; and R. v. J.H.S., (2008), in which the Supreme Court revisits credibility in criminal cases where an accused testifies. These cases are presented with insightful commentary by the authors, offering students a compelling introduction to the subject of evidence.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".