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Record W2289792315 · doi:10.1017/s0305741015001630

Gender Statistics and Local Governance in China: State Feminist versus Feminist Political Economy Approaches

2016· article· en· W2289792315 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

VenueThe China Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsNipissing University
Fundersnot available
KeywordsChinaMainstreamCorporate governancePoliticsState (computer science)Government (linguistics)Political scienceSociologyPolitical economyPublic administrationEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Gender statistics provide an essential tool to mainstream gender equality in policymaking through the recognition by government and the public of gender differences in all walks of life. One legacy of feminist movements since the 1990s has been a focus on the challenges women face to effect substantive equality with men. Based on the findings of a project carried out in three districts of Tianjin, this paper identifies a lack of gender statistics in China's statistical system and the resulting negative impacts on local policymaking. The findings point to weaknesses in the Chinese “state feminist” approach to gender statistics, mostly at the level of the central government. From a feminist political economy perspective, the paper argues, policymaking in China is a process built upon centralized statistical reporting systems that serve the senior governments more than local communities. Gender statistics have the potential to enhance local governance in China when policymaking becomes a site of contestation where community activists demand the use of statistics to assist policies that promote equality.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score0.402

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 studies0.0000.001
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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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