A jurisprudential analysis of the Canadian defence of voluntary intoxication
Bibliographic record
Abstract
The jurisprudence following the defence of voluntary intoxication has remained a controversial topic within the legal system. This thesis evaluates the prominent landmark cases that have shaped the present voluntary intoxication defence and s. 33.1 of the Criminal Code. This Criminal Code section bars the use of the intoxication defence while under extreme intoxication in cases involving harm towards the bodily integrity of another. Section 33.1 has yet to be revisited in the past 20 years. Included in this thesis is the analysis of each precedented case and the majority and dissenting decisions presented by the Supreme Court of Canada. Drawing on case law from both the Ontario Court of Appeal and the Supreme Court of Canada, this thesis is a semi-comprehensive historical timeline of the jurisprudence surrounding the defence. While there are compelling arguments for both the constitutionality and unconstitutionality of s. 33.1, the Supreme Court of Canada has yet to conclude on the long-standing legislation. The Supreme Court will decide on the controversial topic in the upcoming months in an amalgamated hearing of R v Sullivan [2020] and in the case of Thomas Chan. This thesis includes a discussion of possible Supreme Court outcomes for the defence and Criminal Code s. 33.1.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.025 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".