Revisiting pure economic loss: lessons to be learnt from the Supreme Court of Canada?
Bibliographic record
Abstract
This article examines the treatment of pure economic loss claims in England and Canada. The two jurisdictions have much in common. Starting from the same case sources, the common law of each system has struggled to deal with claims for negligently-incurred pure economic loss. Yet, the systems diverged in the 1990s when the Canadian Supreme Court refused to follow the lead of Murphy v Brentwood DC and reiterated its adherence to the Anns two-stage test. It is submitted that, in view of recent developments which suggest the gradual convergence of the two systems, English law should carefully examine the categorisation approach adopted by the Canadian courts. The current English position is far from clear, and the Canadian model is capable of bringing transparency and greater clarity to this difficult area of law.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.019 | 0.039 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".