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Record W2895755850

Regulating Unreliable Evidence: Can Evidence Rules Guide Juries and Prevent Wrongful Convictions

2008· article· en· W2895755850 on OpenAlexfundaboutno aff
Lisa Dufraimont

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaYale University
KeywordsLawPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Recent years have seen increasing concern over the prevalence of wrongful convictions in Canadian criminal courts. This concern is particularly pronounced in jury trials, as jurors are untrained and often lack the familiarity, experience and knowledge required to evaluate evidence of doubtful reliability. Research has suggested that three forms of evidence - eyewitness identification, confessions and jailhouse-informant testimony -pose particular reliability concerns in jury trials. The special problem, common to all three, is the tendency of jurors to overlook the factors that make them unreliable. Canadian criminal evidence law purports to address this problem, but the author argues that the law has hardened into a rigid set of category-based rules that are not particularly conducive to protecting the innocent. Rules that exclude unreliable evidence, as well as rules providing for cautionary instructions or expert testimony on its frailties, all have a place in controlling the risk of wrongful convictions. The author argues that these options should not be treated as strict alternatives. This paper begins with a discussion of the existing approach to eyewitness identification, confessions and jailhouse-informant testimony. It then offers a discussion of the two basic choices that underlie these rules, with each choice involving difficult trade-offs. The "method" choice asks whether educating a jury about the dangers of these types of unreliable prosecution evidence is preferred over limiting a jury's adjudicative freedom. The "knowledge" choice asks whether courts should allow experts to speak to the jury's misguided beliefs, or whether judges should use their own experiences in instructing and cautioning juries. The author is critical of evidentiary rules that are too rigid, and she suggests a flexible regulatory scheme for dealing with these unreliable forms of evidence. She argues that a blend of educating and limiting strategies, and of expert and judicial knowledge, will bring an effective balance that protects the innocent without unduly hindering prosecutors. The author ultimately proposes three approaches that should ground a spectrum of safeguards against the problem of evidentiary unreliability: judicial exclusion of unreliable evidence, jury education about the frailties of evidence associated with wrongful convictions, and the use of expert evidence when judicial instruction would be inadequate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.369
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2008
Admission routes2
Has abstractyes

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