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Record W2521752453 · doi:10.5539/ass.v12n10p106

Social Perspective and Causal Factors Influencing Women Prostitution in Iran

2016· article· en· W2521752453 on OpenAlexvenueno aff
Ehsan Rostamzadeh, Farid Mohseni, Rohani Abdul Rahim

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsLawlessnessCriminologyJurisdictionFeelingPerspective (graphical)PsychologyIslamSocial issuesSocial psychologyPolitical scienceLawGeography

Abstract

fetched live from OpenAlex

<p>This article reviews some factors leading to women prostitution in Iran. Efforts to address this phenomenon in every society must firstly be directed at the individual, the family, and also at economic, social and cultural factors to determine the extent they contribute to sexual crimes. In Iran, sexual offences receive significant attention because of Islamic injunctions application. The country does not permit or tolerate such offences. Since the sexual offences incidents are increasing in the world, this resulted in adverse consequences such as insecurity, the weakening of family structure, offending of public feelings and the prevalence of lawlessness. This makes it necessary, to identify the causal factors that stimulate the occurrence of such offences and to consistently eradicate them.<em> </em>This is an analytical study on the determined topic, to find out how women become manipulated sexually because of the causal factors that influenced them into prostitution activities. Despite the fact that Islamic legal jurisdiction is comprehensive and progressive and prostitution is recognised as an offence in Iran, unfortunately, it seems the offence is sometimes occurring.</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.323
Teacher spread0.305 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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