Spatiotemporal Pattern of Industrialization, Information, Urbanization and Agricultural modernization of Prefecture Level Cities or above in China based on ESDA and GWR
Bibliographic record
Abstract
Taking the industrialization, information, urbanization and agricultural modernization as the measuring indicator, this paper analyses the spatiotemporal pattern, spatial correlations and impact factors of the coordination development of new four modernizations of 343 prefecture-level cities or above in China from 2001 to 2011 by use of ESDA-GWR, gravity center migration, hot-spots analysis and trend surface analysis. The results show as following. The spatial difference of the coordination development of new four modernizations in significantly, showing a trend of the Norththe middle China the South,and the western Chinathe eastern Chinathe middle China. The calculate result of Moran's I shows that the density of the coordination development of new four modernizations 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. All the gravity center of the coordinated development of new four modernizations focus in the central of Henan province, and the center of gravity migration direction is moving northwest first, and then move to the northeast from 2001 to 2011. The most of type of the coordination development of new four modernizations is mild disorders and on the verge of disorder, which shows that the situation of the four modernizations in China is more serious. The hot spots of the four modernizations distribute intensively in east of the coastal zone ofHu Huanyong Line. The impact factors of the four modernizations include per capita investment in fixed assets ratio between urban and rural, per capita gross domestic product, per capita social retail sales of consumer goods, per capita expenditure on education, per capita income ratio between urban and rural, and per capita consumption ratio between urban and rural and so on. Among them, per capita gross domestic product have robust and positive impacts on the coordinating state of new four modernizations, while others impact factors have negative influence on the coordinating state of new four modernizations.
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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 | 0.001 |
| 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".