Canada's Empirically-Based Child Competency Test and its Principled Approach to Hearsay
Bibliographic record
Abstract
For those interested in law reform or a better understanding of the value and limitations of their own legal regime, there is great utility in considering the approaches taken in other jurisdictions to common legal and social problems. This paper offers a comparative perspective on some of the controversies surrounding the treatment of child witnesses, focusing on two areas in which Canadian law has undergone substantial reform and significantly differs from United States law: (1) legislation governing the competence of children to testify; and (2) the common law rules governing the admission of hearsay evidence, especially concerning children’s out-of-court statements regarding abuse. As in the United States, over the past three decades there have been dramatic changes in Canada in the understanding of child abuse, as well as great increases in the number of reported cases of both historic and contemporary child abuse cases, especially child sexual abuse. There have also been very substantial changes in how the justice system treats children. Until the 1980s, Canadian law was premised on the view that child witnesses were inherently unreliable, and very little effort
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".