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Record W2596931606 · doi:10.1177/0097700417697395

Negotiating the Intersection of the Urban-Rural Divide and Gender in Contemporary China: Rural Female University Students

2017· article· en· W2596931606 on OpenAlexaff
Lifang Wang

Bibliographic record

VenueModern China · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Waterloo
FundersSyracuse UniversityAmerican Association of University Women
KeywordsPatriarchyNegotiationAgency (philosophy)Gender studiesChinaSociologyIntersection (aeronautics)Power (physics)Rural areaPolitical scienceGeographySocial science

Abstract

fetched live from OpenAlex

This article, based on a qualitative study of 54 rural female students attending urban Chinese higher education institutions from 2011 to 2012, contests the portrayal of such students as victims suffering from a low level of ability. My research reveals instead that these women exerted agency to recognize, negotiate, and resist both the urban-rural divide and patriarchy, both of which shaped their lives and identities. My research findings also reveal that their lives were multidimensional and diverse, and thus their situations could not be explained by analyzing the effects of either the urban-rural divide or gender alone, but rather by engaging in an analysis of interlocking power structures. The participants’ identities were fluid and in a constant process of formation as they negotiated the various forms of patriarchy they encountered when they moved from their rural homes to attend the urban academies.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.013
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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 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

Citations12
Published2017
Admission routes1
Has abstractyes

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