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Record W2297495122 · doi:10.1111/ilr.12005

Ownership restructuring and wage inequality in urban China

2016· article· en· W2297495122 on OpenAlex
John Whalley, Chunbing Xing

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

VenueInternational Labour Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsCentre for International Governance Innovation
Fundersnot available
KeywordsInequalityEconomicsLabour economicsRestructuringWagePrivate sectorChinaPublic sectorEconomic inequalityWage dispersionEconomic restructuringWage inequalityDemographic economicsEfficiency wageEconomic growthEconomyGeography

Abstract

fetched live from OpenAlex

Abstract Urban income inequality is an increasingly important driver of China's overall income inequality. Using three urban household surveys for 1995, 2002 and 2007, the authors examine how change in enterprise ownership structure – one of the major features of China's economic transition – has contributed to widening urban wage inequality. While wage dispersion was higher in the private sector over the period 1995–2007, it increased faster in the public sector. However, over 50 per cent of the increase in urban wage inequality is associated with labour reallocation from the public to the private sector. Urban wage inequality may thus widen further if more labour is reallocated to the private sector.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.542

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 head0.019
GPT teacher head0.317
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