Spatiotemporal Pattern of Urban-Rural Income Gap of Prefecture Level Cities or Above in China
Bibliographic record
Abstract
Taking the residents income ratio between urban and rural residents as the measuring indicator,this paper analyses the spatiotemporal pattern,global trends,spatial heterogeneities and correlations of income gap of 343prefecture-level cities or above in China from 2000 to 2011 by use of ESDA-GIS,semi-variant function,gravity center migration and trend surface analysis. Spatial lag model is established to estimate the effect of each explanatory variable and analyze the impact factors of urban-rural income gap. The results show as following. The spatial difference of income gap between urban and rural residents is significantly, showing a trend of the middle Chinathe western Chinathe eastern China,and the Centralthe Souththe North. The calculate result of Moran's I shows that the density of urbanrural income gap of prefecture level cities or above in China has a significant and growing global spatial autocorrelation characteristic and spatial cluster,regional disparities trend of income gap is more and more obvious. The gravity center of urban-rural income gap had moved northeast on the whole from 2000 to 2011. The western and northeastern areas are the significantly reduced area in urban-rural income ratio,while the Yellow River basin is the most concentrated areas where the income gap significantly expanded. Cold spot areas of urban- rural income gap have spread markedly,and the spot areas shrink dramatically. Urban- rural human capital inputs ratio,agglomeration of the secondary and tertiary sectors,urbanization rate and spatial lag variable all have positive effects on urban- rural income gap,whereas the urban- rural labor ratio has negative effect on urban-rural income gap.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".