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Record W2157720346 · doi:10.1080/13636820.2014.967797

Vocational training for Liushou women in rural China: development by design

2015· article· en· W2157720346 on OpenAlexaff
Hongxia Shan, Zhiwen Liu, Ling Li

Bibliographic record

VenueJournal of Vocational Education and Training · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVocational educationEmpowermentChinaWorkforceEconomic growthUrbanizationEquity (law)Training (meteorology)IndustrialisationBusinessPolitical scienceEconomicsGeography

Abstract

fetched live from OpenAlex

As industrialisation and urbanisation aggressively take hold in China, all possible labour pools are being tapped to meet the market demands. Liushou women, or women who stay behind in rural areas as their spouses join the massive migrant workforce, are one such labour pool. Vocational training has been adopted by the Chinese state as a development strategy for Liushou women. Against this background, this paper explores how the current vocational training policies and programmes have promoted gender equity for Liushou women. Specifically, it conducts an extensive literature and policy review using a mix of analytical tools: women’s empowerment framework and the social relations approach. Not only does it map the changing social relations within which vocational training for the women is embedded, it also addresses women’s equity in the areas of welfare, access, conscientisation, participation and control.

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)0.007
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.114
GPT teacher head0.380
Teacher spread0.266 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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