The Possibility of “Inference Causation”: Inferring Cause-in-Fact and the Nature of Legal Fact-Finding
Bibliographic record
Abstract
This article defends what it refers to as “inference causation”: a fact-finder’s drawing of a causal link between a defendant’s actions and a plaintiff’s suffering in tort claims in the absence of expert scientific evidence. This type of reasoning, affirmed in 1990 by Justice Sopinka in the Supreme Court of Canada decision, Snell v. Farrell, has encountered significant academic criticism. The author defends inference causation by considering evidence theory. First, he shows that inference causation forms a part of law’s veritism—its commitment to the truth—since legal fact-finding’s aim is always to seek out the best obtainable truth, rather than the absolute truth. Second, he critiques the primacy of scientific evidence by showing that both its reasoning process and the nature of its conclusions are different from those of legal fact-finding. Last, the author shows that all fact-finding—particularly all legal fact-finding—is already inferential. Scientific evidence forms but one of many different elements that are analyzed by fact-finders in their inference about which factual account of the disputed events is the best account. Accordingly, where none is available, the same inference of fact is nonetheless possible.
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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 | 0.053 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.009 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| 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".