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Record W2614180919

JAILHOUSE INFORMANTS IN CANADIAN CRIMINAL COURTS

2017· article· en· W2614180919 on OpenAlexaboutno aff
Olena Beshley

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceCriminologyLawCriminal justiceSociology
DOInot available

Abstract

fetched live from OpenAlex

Criminal justice systems in Canada and around the world have been established to deal with matters that require attention, punishment, and justice. An important function of criminal justice systems is the evaluation of evidence presented in the court of law. Evidence from jailhouse informants who testify that they have been privy to confessions of crimes is a contentious issue. Much of the scholarly literature available to date on wrongful conviction cases focuses on causes of insufficient and unreliable evidence obtained through different techniques and from different sources. Despite the high number of investigations into wrongful conviction cases, the subject of jailhouse informants has not yet been thoroughly explored as a leading cause of wrongful convictions in Canada. The current study employed a qualitative methodology in analyzing reported criminal cases that have used jailhouse informant’s testimony in order to find parallels with respect to cases, informants, and testimonies. The findings center on the credibility of evidence, the trustworthiness of informants, judicial cautions and the consequences that followed the use of such evidence. While jailhouse informants have been identified as a cause of wrongful convictions, there have been few studies that provide insight into these cases in Canada. The present study focuses exclusively on the influence of jailhouse informants (also known as in-custody informants) and their role and impact on decision-making in criminal trials. Further implications of the findings on potential miscarriages of justice are also discussed.

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)0.000
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 score0.851
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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