Vol. 23 Dalhousie Journal of Legal Studies 191 COMPLICAT ING THE S IMPLE PROBABIL ITY PRINCIPLE: DEVELOPING A NEW APPROACH TO PROBABIL IST IC REASONING IN PERSONAL INJURY L IT IGAT ION
Bibliographic record
Abstract
Canadian courts use simple probability reasoning inconsistently in personal injury litigation, subjecting litigants to irregular legal principles and potentially improper compensation. Turning to foundational principles of tort litigation, I suggest a new framework for the availability of simple probability that would promote greater coherence. Simple probability reasoning is under-stood as an alternative standard of proof that enables compensation for a loss proportional to the likelihood that the loss will occur. Accordingly, the avail-ability of simple probability is thought to depend on which types of facts (past vs. future vs. hypothetical facts) are amenable to balance of probabilities proof versus simple probability. This is the ‘type of fact ’ framework, but it is not applied consistently. Part 1 argues that the inconsistency is rooted in the mischaracterization of simple probability reasoning as a standard of proof. It is better conceived of as a method of enabling chances, in their own right, to become legally relevant facts. Understood this way, simple probability is available only where chances are relevant to the legal determination at stake. I apply this characterization in Part 2, concluding that while simple probability reasoning is irrelevant to liability determinations, it is crucial in appropriately assessing damages.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.100 | 0.008 |
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".