Anchoring the Law in a Bed of Principle: A Critique of, and Proposal to Improve, Canadian and American Hearsay and Confrontation Law
Bibliographic record
Abstract
As recent case law demonstrates, both American Sixth Amendment Confrontation Clause jurisprudence and Canadian common law relating to hearsay evidence are conceptually problematic. The laws are, at times, internally incoherent and are difficult to justify on the basis of legal principles. This Article critiques confrontation and hearsay law in the United States and Canada, respectively, by exposing the lack of principle underlying each body of law. The Article develops a principled basis for evidence law in general, and hearsay and confrontation law in particular, providing a more stable foundation for hearsay and confrontation frame-works. Ultimately, the Article argues that the epistemic, truth-seeking goal of criminal evidence law is best served by the broad admission, rather than exclusion, of all hearsay evidence. Furthermore, while fairness concerns are relevant to some rules of evidence, there are no valid fairness concerns operating in the context of hearsay and confrontation law that should displace the primary principle of facilitating and promoting epistemically accurate fact-finding in criminal trials. Finally, this Article suggests that any dangers associated with the broad admission of hearsay evidence can be mitigated through effective argument by counsel and appropriate cautions to the trier of fact regarding any weaknesses inherent in the evidence.
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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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.023 | 0.101 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.019 | 0.024 |
| 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".