The Possibility of “Inference Causation”: Inferring Cause-in-Fact and the Nature of Legal Fact-Finding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it