Limitation Periods for the Enforcement of Foreign Judgments: Laasch v. Turenne
Bibliographic record
Abstract
Laasch v. Turenne raised important questions about the available options for the enforcement of foreign judgments in Alberta and emphasized the need for foreign judgment creditors to act very quickly indeed to secure such enforcement. Nathan Laasch was just 16 years old when, in November 2000, he suffered heart failure which resulted in his serious and permanent disability. He lived in Montana and had attended the office of the defendant, Dr. Turenne, on two occasions complaining of episodes of a rapid heart rate, chest discomfort, and lightheadedess. Dr. Turenne also lived in Montana where she practised medicine. She had apparently concluded that she could not diagnose the cause of Nathan’s problems, but nonetheless prescribed and administered a beta-blocker. It transpired that Nathan was suffering from Wolff-Parkinson-White syndrome and that beta-blockers were contraindicated for that disease.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.024 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".