Bibliographic record
Abstract
Evil has been a diffıcult presence to shake in the judicial treatment of Parliament’s criminal law power, s. 91(27). From its early treatment by the Judicial Committee of the Privy Council to the Supreme Court of Canada’s latest disagreements in Reference re Genetic Non-Discrimination Act, the necessity of suppressing evil has woven in and out of the jurisprudence of the criminal law power. Alluring for its potential to provide some integrity and definitional limits to a broad head of jurisdictional power, a judicial standard premised on evil ultimately distracts more than it assists in adjudicating the division of powers by drawing courts into unquantifiable assessments of the amount of evil required before Parliament can validly enact criminal law. Better for courts to be guided by the broader conception of criminal public purpose articulated in Justice Rand’s famous judgment in Margarine Reference as a way to enable the respect of the full scope of Parliament’s authority while also protecting the balance of federalism. The Supreme Court’s divided reasons in Reference re Genetic Non-Discrimination Act provide hope for just that approach while also suggesting that evil may continue to unhelpfully hover at the edges of a case law it has haunted for too long.
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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".