Probate Actions and 'Suspicious Circumstances': A Third Standard of Proof for Allegations Involving Moral Guilt
Bibliographic record
Abstract
When a will is challenged as being executed under suspicious circumstances, Canadian courts have historically sought clear, compelling, and cogent evidence to demonstrate the will’s validity. The associated standard of proof has been described as one residing beyond a balance of probabilities, and is conceptualized as the ‘third standard of proof’ in addition to the civil and criminal standards. This third standard of proof is also particularly appealing when allocating the risk of error in an estates context in which testators are deceased and no longer available to clarify their intentions or perspectives. However, after the 2008 Supreme Court of Canada decision, FH v McDougall (“McDougall”), it was resolutely pronounced that only two standards of proof operate in Canada, with the third standard of proof dismissed for the practical problems of its application. As conceded below, there are compelling and valid reasons to disregard a third standard of proof for typical will challenges investigating circumstances such as the execution of the will or the testamentary capacity of the testator. This paper argues that for challenges that involve allegations of moral guilt, and in cases of fraud or undue influence over the testator, then something more then a balance of probabilities is desirable, and the more demanding third standard of proof should be utilized.
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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 | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".