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Record W2160800665 · doi:10.7202/1015293ar

Au-delà de la criminalisation : l’immigration et les enjeux pour la criminologie

2013· article· fr· W2160800665 on OpenAlexaffvenueabout
João Velloso

Bibliographic record

VenueCriminologie · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Le but de cet article est de discuter de l’importance croissante des punitions administratives dans le champ pénal, à partir de la judiciarisation des conflits d’immigration au Canada. À l’aide d’une analyse documentaire et des résultats d’une enquête de terrain menée à la Commission de l’immigration et du statut de réfugié du Canada entre 2007 et 2009, nous présenterons certaines caractéristiques de la mise en forme des litiges en droit de l’immigration et de leur façon de punir et nous soutiendrons que celles-ci diffèrent substantiellement de celles propres à la mise en forme pénale. Notre objectif ultime consistera à problématiser l’idée de criminalisation de l’immigration comme une catégorie capable de nuancer la complexité des formes de réaction sociale administratives. Nous suggérerons qu’il faut plutôt appréhender la punition en droit administratif comme telle (mesures de police et sanctions administratives) et repenser son rôle au sein du champ pénal, et ce, afin de mieux comprendre l’ensemble des réactions sociales dans les différentes institutions juridico-politiques, l’interaction et la complémentarité de celles-ci ainsi que leurs logiques de gouvernance, de mise en forme et leurs implications sociales.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.293
GPT teacher head0.413
Teacher spread0.120 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2013
Admission routes3
Has abstractyes

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