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Record W3139652525 · doi:10.29173/mlj993

Jackpot: The Hang-up Holding Back the Residual Category of Abuse of Process

2017· article· en· W3139652525 on OpenAlexaboutno aff
Jeffery Couse

Bibliographic record

VenueManitoba Law Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtMisconductRedressLawSubstantive due processPolitical scienceDue processCriminologySociologyCommon law

Abstract

fetched live from OpenAlex

The abuse of process doctrine allows courts to stay criminal proceedings where state misconduct compromises trial fairness or causes ongoing prejudice to the integrity of the justice system (the "residual category").The Supreme Court revisited the residual category in the 2014 case R v Babos.In Babos, the Supreme Court provided a three-stage test for determining whether an abuse of process in the residual category warrants a stay of proceedings.This article critically examines Babos and its progeny.Notwithstanding the Supreme Court's insistence that the focus of the residual category is societal, all three stages of the test remain disconcertingly preoccupied with the circumstances of the individual accused.Courts' reluctance to give undeserving accused the "jackpot" remedy of a stay has prevented the court from dissociating itself from state misconduct.Instead, courts have imposed remedies which inappropriately redress wrongs done to the accused.This paper suggests four ways for courts to better advance the societal aim of the residual category.First, a cumulative approach should be taken to multiple instances of state misconduct rather than an individualistic one.Second, courts ought to canvass creative remedies in considering whether an * This paper was written in a personal capacity and does not in any way reflect the views of the Ontario Superior Court of Justice or the Ministry of the Attorney General.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.090
GPT teacher head0.358
Teacher spread0.269 · 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 designQualitative
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

Citations0
Published2017
Admission routes1
Has abstractyes

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