What the United States Taught the Commonwealth About Pure Economic Loss: Time to Repay the Favor
Bibliographic record
Abstract
This paper deals with a discussion of the comparative treatment of the recovery of pure economic loss in negligence, a topic that has dominated my scholarship for more than thirty years.'My purpose is more to discuss how my research in this area has been informed by comparative study than to articulate the relevant rules and principles themselves.Some of the points of substance I will make will be contentious, and I will leave those debates to other publications and other times.I hope this makes today's topic more interesting.Many American tort lawyers would not recognize "economic loss" as an organizing principle for tort law; or if they did, they might not restrict the topic to negligence law.In what I will loosely call the Commonwealth-England, Canada, Australia, and New Zealand-most lawyers would understand this subset of negligence law to include cases of the following types: 2 1. Negligent misrepresentation brought, for example, where a nonprivity third party sues for losses suffered from investments made in reliance on negligently prepared corporate financial statements.2. Negligent performance of professional services where perhaps a frustrated beneficiary sues the solicitor who negligently drafted an impugned will., 3. Relational economic loss brought by a plaintiff who suffers
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".