Assessing Credibility:\nThe Impact of a Motive to Lie and the Embellishment\nof Evidence — the Canadian Approach
Bibliographic record
Abstract
In R. v. Bowers, the Alberta Court of Appeal suggested that it has been “recognized that the process of assessing credibility cannot be reduced to or confined by legal rules . . . [and] there is no fixed set of rules to use in assessing the credibility of a witness.” 2022 ABCA 149, paras. 39-40 (Can.).\nTwo exceptions to this general rule involve a witness having a motive to lie and the embellishment of evidence by a witness. In both instances, a witness’s credibility will general be negatively impacted. However, what if the opposite occurs? What if a trial judge concludes that the witness did not embellish their evidence? What if there is no evidence that the witness, particularly a complainant, had a motive to falsely implicate the accused? How are trial judges to deal with these scenarios? These issues have recently been the subject of considerable appellate commentary in Canada, including by the Supreme Court of Canada.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".