Case Comment: Attorney General for Saskatchewan v Lemare Lake Logging Ltd., 2015 S.C.C. 53
Bibliographic record
Abstract
A comment on the Supreme Court of Canada decision in Attorney General for Saskatchewan v Lemare Lake Logging, 2016 SCC 5. This case arose out of an application by Lemare Lake Logging Ltd. to the Saskatchewan Court of Queen’s Bench for the appointment of a receiver and manager of 3 L Cattle Company Ltd., pursuant to section 243 of the federal Bankruptcy and Insolvency Act. 3 L Cattle argued that Lemare Lake failed to comply with Part II of the provincial Saskatchewan Farm Security Act, which requires a secured creditor to seek leave before appointing a receiver, and that as a result the application for a receiver was a nullity. The constitutional question arose as to whether there was a conflict between the federal and provincial statutes. At trial the judge found no conflict, and ruled in favour of 3 L Cattle. The Saskatchewan Court of Appeal found that the provincial statute frustrated the purpose of the federal receivership regime. The Attorney General of Saskatchewan appealed the decision to the Supreme Court of Canada. This case comment discusses the Supreme Court of Canada’s 6-1 Majority decision, which found that the Saskatchewan Act did not frustrate the purpose of the federal receivership regime. It also discusses Justice Cote’s dissent, and its implications for provincial autonomy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.034 | 0.018 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".