The Law of Nuisance in Canada, Gregory S Pun & Margaret I Hall (Markham, Ont: LexisNexis, 2010)
Bibliographic record
Abstract
“There is perhaps no more impenetrable jungle in the entire law,” Dean Page Keeton once wrote, “than that which surrounds the word ‘nuisance.”’ Such impenetrability may explain steps taken in England to subsume nuisance law into the fault-based law of negligence. It does not take much imagination to appreciate why the English would move in that direction. At some point, after all — Dean Keeton’s jungle floor having become impossible to navigate — practitioners and judges must be tempted to join the academic lawyers who gaze down from the treetops. The perspective from the canopy might well persuade them that their problem is not amenable to the small doctrinal fix of cutting a path through a grove or two. Rather, it is more fundamental, going to the very lay of the land. They therefore abandon nuisance law’s tangled thickets altogether, preferring — to bring the metaphor home now for a decent burial — the broader and more familiar paths of negligence 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".