Tate & Lyle: Pure Economic Loss and the Modern Tort of Public Nuisance
Bibliographic record
Abstract
Professor Lewis Klar criticizes the Canadian approach to the tort o f public nuisance for being illogical and incoherent. The authors agree with Klar's assessment o f the current state of public nuisance law, but argue that insights drawn from the House o f Lords decision in Tate & Lyle Industries Ltd. v. Greater London Council offer a way forward. By conceptualizing the tort o f public nuisance as a cause o f action that protects subjects from suffering actual loss that is consequential on the violation of their passage and fishing rights over public property, Tate & Lyle offers a coherent and restrained formulation o f the tort o f public nuisance. This article examines the Tate & Lyle approach to public nuisance and applies it to two infamous Canadian public nuisance cases. It concludes that the coherent, logical approach to public nuisance articulated by the House o f Lords in T ate & Lyle should be readopted by Canadian courts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| 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".