Bibliographic record
Abstract
Nowadays, everything is always someone else’s fault. In the fault-based tort system in Canada, that often translates into injury cases exhibiting greater tendencies for presenting with complex information about how an injured victim’s injuries were caused. In many instances, the additional causation information is unwanted, irrelevant and muddies the task of applying causation doctrine to the facts of a particular case. This unwanted information about causation that distracts from the application of causation law to case facts can be thought of as “causal noise.” It needlessly complicates an already complicated process. Injured accident victims often have pre-existing injuries or health conditions that confounds the causal inquiry necessary in order to bring a tort claim under negligence law. Some victims have the misfortune of experiencing multiple, successive accidents over time which affect end result injuries and the healing process. It can often seem impossible to sort out the multiple causal factors – both negligent and non-negligent – that result in an injured victim’s total constellation of symptoms comprising his or her injury. These multiple competing and contributing causal factors can make up causal noise. Their very existence distracts the fact-finder and litigants with complicated legal doctrine and intricate factual scenarios when, in fact, the inquiry is supposed to be one about the effect of the defendant’s at-fault behaviour on the plaintiff. This article attempts to offer some solutions to containing causal noise by simplifying the thinking about causation so as to maintain tort law’s fault-based compensatory goals.Part I of this article is a causation primer, discussing the present development of causation doctrine in Canadian negligence law. This Part sets the backdrop for the normative discussion to follow. Part II of this article touches on a number of issues germane to personal injury cases including which doctrinal test to use – “but for” or material contribution – and how to compartmentalize the “but for” test into an understandable concept useful in cases exhibiting causal noise. Additionally, the article advocates for trial judges to revitalize the approach of drawing causal inferences in cases involving causal draws, which are cases where the evidence appears equally balanced, but insufficient, for both plaintiff and defendant to prove causation to the requisite degree. Finally, the article sketches a framework for sorting out causal noise in difficult cases involving multiple competing causal factors. The framework is applicable not only in terms of causation but also in terms of assessing a defendant’s responsibility for the extent of a victim’s harm and the responsibility to pay for that harm.
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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".