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Record W2150857302 · doi:10.1177/1466802503003001457

Trafficking of women for marriage in China

2003· article· en· W2150857302 on OpenAlexaff
Gracie Ming Zhao

Bibliographic record

VenueCriminal Justice · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDignityChinaRestructuringIdeologyDominance (genetics)SociologyPatriarchyCriminologyPoliticsPolitical scienceGender studiesDevelopment economicsEconomic growthLawEconomics

Abstract

fetched live from OpenAlex

Trafficking of women for marriage is recognized as a modern-day slave trade, although it has a long history in China. The reasons for its resurgence in China include, but are not limited to, patriarchal values, state-tolerated sex discrimination, vulnerability of women and the transformation of socio-economic situations. Accordingly, the task of eradicating the trafficking in women involves combating feudal and patriarchal assumptions about male dominance and male supremacy; building up the confidence and dignity of the gender of female; systematic governmental and international support of issues of importance to women; restructuring legal systems where they are still imperfect; adjusting economic systems so that women are never exploited economically; strengthening fundamental and higher education of women; regulating objectifying and pornographic media images of women; and developing ways in which men and women can relate without either dependency or dominance. The author examines and evaluates both the strengths and weaknesses of the current criminal justice policy of the Chinese government against trafficking practices. She then argues that the existing policy against trafficking is insufficient and ineffective and needs to be reformed in a number of aspects as suggested. In this article, the author takes a multi-disciplinary approach, including historical, cultural, economic, ideological, sociological and legal study.

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)0.001
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 score0.075
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.326
Teacher spread0.298 · 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

Citations53
Published2003
Admission routes1
Has abstractyes

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