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Record W2291615838 · doi:10.3390/laws5010012

Can a Patriarchal World Be Corrected by a Criminal Law? Feminist Struggles, Penal Justice and Legal Reform in France (1970–1980)

2016· article· en· W2291615838 on OpenAlexaff
Jean Bérard

Bibliographic record

VenueLaws · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité de MontréalInternational Centre for Comparative Criminology
Fundersnot available
KeywordsPunishment (psychology)OffensiveLawCriminal justiceCriminal lawCriminologyPolitical scienceCriticismSexual violenceEconomic JusticeSociologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

This article describes (1) the relationship between the demands made by feminist movements of the 1970s in cases of sexual violence and criticism of the criminal justice system by these movements and other groups, including the prisoners’ movement; and (2) the relationship between this debate and the legal process of reforming the definition and punishment of rape. Two periods are analyzed. In the early 1970s, the common cause of very different movements targeting the law was the priority given to the defense against forms of repression and disciplinary institutions. After 1975, the demands of feminist and prisoner movements diverged and even conflicted. One camp called for an offensive approach to changing the legal punishment of rape whereas the other camp fought against penal reforms imposed by the government and, more specifically, against long sentences.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.182
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.024
Scholarly communication0.0080.004
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.225
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2016
Admission routes1
Has abstractyes

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