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Record W2977379601 · doi:10.7202/1064656ar

Punishing while Presuming Innocence : A Study on Bail Conditions and Administration of Justice Offences

2019· article· en· W2977379601 on OpenAlexaffvenue
Marie Manikis, Jess De Santi

Bibliographic record

VenueLes Cahiers de droit · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsPunitive damagesPresumption of innocenceInnocencePunishment (psychology)ScapegoatPolitical scienceLawCriminologyAdministration (probate law)Administration of justiceCriminal justicePresumptionEnforcementEconomic JusticeSociologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

This paper examines the process and outcomes of bail hearings, focusing on cases where defendants’ hearings involved administration of justice and sentencing offences. The data analyzed for this project suggests that despite the presumption of innocence and non-punitive official objectives of judicial release, the practice by law enforcement and courts at this stage of the process tends towards punitiveness. These punitive responses are illustrated by three main findings that relate to the detention of individuals accused of administration of justice or sentencing offences, the number of conditions of release breached per combination of charges, and the breached conditions of release. These processes are understood through a durkheimian lens, using Fauconnet’s work which considers the social function of punitive processes as focused on annihilating the criminal act to establish social order. As will be seen, this function is achieved through the selection of a scapegoat that is rapidly punished, rather than appropriately assigning individual liability.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.302
Teacher spread0.281 · 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 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

Citations5
Published2019
Admission routes2
Has abstractyes

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