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Record W2771885654 · doi:10.1080/13545701.2017.1404621

Gender, Low-Paid Status, and Time Poverty in Urban China

2017· article· en· W2771885654 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFeminist Economics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of Winnipeg
FundersNational Natural Science Foundation of ChinaNational Bureau of Statistics of China
KeywordsPovertyOvertimeChinaDemographic economicsTime-use surveyWageEconomicsSurvey data collectionLabour economicsEconomic growthWork (physics)Geography

Abstract

fetched live from OpenAlex

Using synthetic data from the 2008 China Time Use Survey (CTUS) and the 2008 China Household Income Project (CHIP), this study estimates time-poverty rates and compares the profiles of time-poor men and women workers in urban China. In line with previous research, time poverty is defined as a lack of enough time for rest and leisure. Three time-poverty measures are adopted. By all three measures, women paid workers and low-paid workers account for a disproportionate share of the time poor. Regression analysis further shows that, other things being equal, workers who are women, low-paid, married, and who live with children or the elderly in counties with higher overtime rates and lower minimum wage standards are more likely to be time poor. Simulations indicate that enforcing working time regulations and raising minimum wage standards could be effective for reducing time poverty.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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