Revisiting pure economic loss: lessons to be learnt from the Supreme Court of Canada?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it