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Record W2282160445 · doi:10.5539/ass.v12n3p130

Sadistic Sexual Offenses in Criminal Cases of Iran and France

2016· article· en· W2282160445 on OpenAlexvenueno aff
Seyedmohammad Mousavi, Yousef Jafarzadi, Shamsollah Khatami, Arash Babaei

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologyImprisonmentDignityPunishment (psychology)HarassmentSadistic personality disorderSexual violenceCriminal lawPsychologyLawPolitical scienceSocial psychologyPersonalityPersonality disorders

Abstract

fetched live from OpenAlex

Crimes, especially crimes of sexual violence is a problem in every society, in the midst of violent crimes, especially rape, beatings and even death for sex by the psychological impact on creating a sense of insecurity in society the dignity and respect most influential crime is. Sadistic crimes, including cases of sexual violence in the country's laws, particularly the law of France and Iran have been severely. Sexual harassment and sexual violence in France has a mild to severe penalties that depend on the type of crime and its dissemination. So that kind of punishment in relation to crimes of sexual violence are synthetic and financial penalties and even imprisonment is involved. The laws of the country also showed that sexual violence to it that French law has the details of the punishment, has not been raised, but sexual violence in the form of psychological violence by criminal penalties and imprisonment are required. The laws of the country, violence against women and children in two after payment of blood money or the lives of members and in case of immoral nature of the punishment of flogging and death will follow.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.042
GPT teacher head0.343
Teacher spread0.301 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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Same venueAsian Social Science→Same topicPsychopathy, Forensic Psychiatry, Sexual Offending→French-language works237,207→