Normativity, Fairness, and the Problem of Factual Uncertainty
Bibliographic record
Abstract
This article concerns the problem of factual uncertainty in negligence law. We argue that negligence law's insistence that fair terms of interaction be maintained between individuals--a requirement that typically manifests itself in the need for the plaintiff to prove factual or "but-for" causation--sometimes allows for the imposition of liability in the absence of such proof. In particular, we argue that the but-for requirement can be abandoned in certain situations where multiple defendants have imposed the same unreasonable risk on a plaintiff, where the plaintiff suffers the very sort of harm that rendered the risk unreasonable, and where the plaintiff cannot prove which of the defendants was the but-for cause of her loss. This approach provides one way to understand the Supreme Court of Canada's recent decision in Resurfice Corp. v. Hanke. We find support for our approach in various concepts that underlie negligence liability quite generally. These underlying concepts are normative in nature, and manifest core notions of justice and fairness. We argue that approaches to the problem of factual uncertainty that appeal to such normative principles to make sense of atypical cases of causation are in no way inconsistent with the nature and structure of negligence law. Rather, the opposite is true: in taking negligence law seriously as law, such approaches are instead reflective and supportive of it.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".