MétaCan
Menu
Back to cohort
Record W2290184632

Comparative Reflections on Miscarriages of Justice in Australia and Canada

2015· article· en· W2290184632 on OpenAlexaffabout
Kent Roach

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolitical scienceAppealLegislatureLawParliamentAccountabilityScholarshipSupreme courtLaw reformEconomic JusticePolitics
DOInot available

Abstract

fetched live from OpenAlex

This article identifies comparative scholarship as a promising way to understand the causes of and remedies for wrongful convictions. The article starts by suggesting that a string of high-profile DNA exonerations and public inquiries examining their systemic causes have led to Canadian judges and prosecutors accepting the reality of wrongful convictions more readily than most of their Australian counterparts. The next part of this article suggests that Australian legislatures have been more active than the Canadian Parliament in regulating police and prosecutorial behavior that contributes to wrongful convictions. In turn, the Canadian judiciary has been more creative in responding to the causes of wrongful convictions than the Australian judiciary. This theme is carried over to the next part which examines Australian legislative innovations such as second appeals based on fresh and compelling evidence and mechanisms for courts to conduct their own inquiries. Except for some 2002 reforms to the petition procedure, most reforms in Canada have come from the courts. They include the Supreme Court of Canada hearing fresh evidence or remitting cases to Courts of Appeal to do so and the granting of bail pending petition decisions by the executive and judicial review of such decisions. Australia and Canada can learn from each other in order to ensure that both legislatures and courts respond to wrongful convictions and that, where appropriate, there be both systemic and individual accountability for wrongful convictions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.101
GPT teacher head0.389
Teacher spread0.288 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations28
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicJudicial and Constitutional StudiesFrench-language works237,207