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Record W2615765476 · doi:10.4000/champpenal.9501

La construction politique des attentes victimaires dans les débats parlementaires entourant la création de la loi pénale

2017· article· fr· W2615765476 on OpenAlexaffabout
Richard Dubé, Margarida Garcia

Bibliographic record

VenueChamp pénal · 2017
Typearticle
Languagefr
FieldMedicine
TopicHistorical and Scientific Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Cet article présente les résultats d’une recherche menée au Canada sur les références aux victimes et à leurs attentes dans les débats parlementaires entourant la création de la loi pénale. Il problématise à cet égard le rôle du programme répressif de la rationalité pénale moderne dans la mise en forme d’attentes victimaires répressives. Dans le cadre des projets de loi étudiés, ce sont les attentes de dissuasion qui sont les plus souvent évoquées : des peines plus sévères sont présumées plus dissuasives et à même d’assurer une meilleure protection des victimes. À ces attentes de protection s’ajoutent par contre des attentes de reconnaissance qui, au sein du programme de la rationalité pénale moderne, semblent faire intervenir une nouvelle théorie de la peine, théorie que nous avons désignée par l’expression « théorie de la reconnaissance de la dignité victimaire ». L’article en précise les dimensions conceptuelles et les indicateurs empiriques tout en en problématisant les enjeux sur le plan de l’évolution du droit criminel moderne.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.336
Threshold uncertainty score0.668

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.030
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.040
GPT teacher head0.316
Teacher spread0.276 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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