Bibliographic record
Abstract
This article about causation in negligence law is different from past attempts at unraveling causation in Canada. It argues that there is nothing overly confusing about the law of causation in negligence. Rather than lament the confusing state of affairs or argue for a new causation test, the article attempts to define the current state of causation in Canadian negligence law with a simple goal in mind – to have a clearer, more productive conversation about the law with the fundamental concepts clearly and unobtrusively on the table. Such clarification should hopefully augment and streamline discussions among courts, commentators, and lawyers about this seemingly thorny subject. To date, writings about causation in tort have focused largely on the mess of the entire subject and how so much is confusing and undefined. This article proceeds on the foundation that the leading Canadian cases on causation should not be read like cryptic advice from isolated fortune cookies, with each word taking on ominous significance. The cases are a continuum of conversations about an important topic in tort law. This article offers a cohesive framework to the law by taking a longitudinal perspective and focusing on the simple themes of Canadian tort law present in the causation jurisprudence: the doctrinal tests for causation, evidence for proving causation, thin skulls, and crumbing skulls. Avoiding emphasis on a case-by-case dissection approach, this article instead combines the relevant jurisprudence in an understandable scope. At the centre of the analysis is the bedrock principle that the negligence system is a fault-based system which relies on proving a connection between a defendant’s wrongful behaviour and a plaintiff’s injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".