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Record W7007962777

Alcohol use disorders and crime: identifying and analysing the role of judicial discourse

2019· dissertation· en· W7007962777 on OpenAlexaffabout

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsTheme (computing)Criminal justiceAlcohol consumptionCriminal behaviourAddictionEconomic JusticeCriminal codeState (computer science)
DOInot available

Abstract

fetched live from OpenAlex

Alcohol consumption is an activity enjoyed responsibly by most.However, for those struggling with addiction, alcohol may quickly become a catalyst for behaviour defined by law as criminal.In fact, the majority of criminal offenders in Canada suffer to some degree from an alcohol use disorder.Given the prevalence of addiction among criminal offenders, the Canadian criminal justice systems and its judges should be prepared to best deal with addicted offenders.This thesis will outline some of the most common responses and themes of discussion among judges who are faced with an offender suffering from an alcohol use disorder.Further, key terms relating to alcohol use disorders, crime and sentencing will be defined.The original research and analysis of this thesis will ultimately enable the first classification of its kind of judicial discourse on alcohol use disorders by theme of discussion, thus enabling an analysis of the impact that these various strains of discussion may have on the outcome of the accused or offender.The themes identified include incapacitation, recognition of rehabilitative efforts, acknowledgment of state responsibility, alternative treatment, and discussion of reform.Beyond the academic specificities of this study, this thesis has attempted to articulate the possibility that the criminal may not always be solely responsible for a crime and that society may have a contributory role in labelling the individual as criminal.Furthermore, the 'criminal' may simply be an addict.La consommation d'alcool est une activité appréciée d'une manière responsable par la plupart des gens.Cependant, l'alcool peut rapidement devenir un catalyseur pour les comportements qui sont qualifiés comme étant criminels par la loi.En fait, la majorité des délinquants au Canada souffrent dans une certaine mesure d'un trouble lié à l'alcool.Étant donné la prévalence de cette dépendance dans le système, comment le système de justice pénale canadien et ses juges traitent-ils le délinquant alcoolique?Quels sont les réponses et les thèmes de discussion les plus courants parmi les juges confrontés à un délinquant souffrant d'un trouble de l'alcoolisme?Cette thèse définira les termes clés relatifs aux troubles de la consommation d'alcool, à la criminalité et à la détermination de la peine.La recherche et l'analyse originale de cette thèse permettront au final la première classification du discours judiciaire sur les troubles de la consommation d'alcool par thème de discussion, permettant ainsi d'analyser l'impact de ces différentes tensions de discussion sur la détermination de la peine de l'accusé ou du délinquant.Les thèmes identifiés comprennent l'incapacité, la reconnaissance des efforts de réadaptation, la reconnaissance de la responsabilité de l'État, le traitement alternatif et la discussion sur la réforme.Au-delà des spécificités académiques de cette étude, cette thèse a tenté d'expliquer la possibilité que le criminel ne soit pas toujours seul responsable d'un crime et que la société puisse jouer un rôle contributif en qualifiant l'individu de criminel.En outre, le «criminel» peut simplement être un toxicomane.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.007
Science and technology studies0.0170.021
Scholarly communication0.0180.010
Open science0.0030.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.316
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes2
Has abstractyes

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