Defining the Scope of the Hearsay Rule in Criminal Cases: A Comparative Perspective
Bibliographic record
Abstract
Abstract This chapter offers, from a comparative perspective, a consideration of possible approaches to defining the scope of the hearsay rule in criminal cases. In The Principles of Criminal Evidence (1989), Adrian Zuckerman called for a more flexible approach to criminal hearsay doctrine than that prevailing in England and Wales at the time. Some three decades later, the major common law jurisdictions retain rules that have the effect, broadly speaking, of presumptively excluding hearsay evidence in criminal cases. There has been considerable judicial and academic focus in recent times on issues associated with the exceptions to such exclusionary rules. This chapter examines a related question that, although fundamental, has attracted far less attention and remains relatively under-explored: what is, and what should be, the precise scope of the rules that presumptively exclude hearsay evidence in criminal cases? It is noted that the decision of the Supreme of Court of Canada in R v Baldree (2013) offers a radically different approach to this question from that taken in the Criminal Justice Act 2003 (England and Wales), the US Federal Rules of Evidence, the Australian uniform evidence legislation, or the Evidence Act 2006 (New Zealand). In the light of a consideration of the approaches taken in various jurisdictions and the implications of these approaches, the chapter concludes that the Canadian approach provides the most sensible basis for possible reform. Some suggestions on the way in which the relevant law in England and Wales might be reformed are also offered.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| 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".