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Record W2569307646 · doi:10.5430/wjel.v6n4p8

Prostitution: The Enconomics of Sex and Power Dynamics in El Saadawi’s Woman At Point Zero, Adimora-Ezeigbo’s Trafficked and Unigwe’s On Black Sisters Street

2016· article· en· W2569307646 on OpenAlexvenueno aff
Oyeh O. Otu

Bibliographic record

VenueWorld Journal of English Language · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsSubjectivityAgency (philosophy)Power (physics)Gender studiesHuman sexualityPosition (finance)SociologySanctionsEconomic powerPolitical scienceCriminologyLawPoliticsBusiness

Abstract

fetched live from OpenAlex

Many feminist writers and critics have projected female prostitution as a radical and aggressive strategy aimed at undermining patriarchal values and wresting power and subjectivity from men. Many have argued that through prostitution women revolt against the traditional double standards which on one hand grant men license to be sexually adventurous, promiscuous and unfaithful to their partners, and on the other hand legislate and enforce grave moral and social sanctions against women who engage in the same acts. Such critics aver that women move from the position of passive sex objects designed for men’s sexual pleasures to the position of agency and subjectivity that enable them express their sexuality, and more importantly use their bodies to turn men to objects of sexual and economic exploitation. But this paper argues that sex is a huge industry ultimately controlled by men. The three African novels studied here reveal that from sex tourism, ownership and management of hotels and brothels, to the mafia-like transnational business of trafficking in women, men control the sex industry, and that prostitution, by objectifying and commodifying the woman’s body, makes women (female prostitutes to be specific) objects of sexual and economic exploitation and victims of modern day slavery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.244
Teacher spread0.238 · 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 teacher head, 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

Citations1
Published2016
Admission routes1
Has abstractyes

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