Conflicting Stories and Reasonable Doubt: Variations on W. (D.)'s Theme
Bibliographic record
Abstract
Whether the guilt of an accused has been proven beyond a reasonable doubt is always a difficult issue, particularly so when the accused has testified. There is little difficulty when an accused's exculpatory testimony is accepted by the trial judge, since that of course leads unambiguously to an acquittal. More complex is the situation where a trial judge does not simply accept the accused's version of events — that is, most of the time. In those circumstances, trial judge must embark down the twisty road of deciding whether disbelieved testimony can nonetheless result in an acquittal, or alternatively whether an acquittal must still result from some other reason.\nThe primary guidance in these circumstances is the Supreme Court of Canada's decision in R. v. W. (D.). In addition, though, a number of recent court of appeal decisions in several provinces have added guidance on applying those rules in various circumstances. These decisions show the delicate balancing that is necessary to, on the one hand, permit reasonable inferences, but on the other continue to respect the need to require that convictions not occur without proof of guilt beyond a reasonable doubt.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".