Measurement Research on New Urbanization Development of Changsha-Zhuzhou-Xiangtan City Cluster Based on the Concept of “Production-Living-Ecology” Space
Bibliographic record
Abstract
With the accelerating urbanization process, the urbanization rate is increasing year by year. Taking Changsha-Zhuzhou-Xiangtan City Cluster as an example, the evaluation index system of new urbanization in Changsha-Zhuzhou-Xiangtan City Cluster is constructed from three dimensions: production space, living space and ecological space. The entropy method, mathematical statistics analysis and exploratory spatial data analysis are adopted, and the time from 2008 to 2020 is taken as the research time node. This paper analyzes the development level of new urbanization in Changsha-Zhuzhou-Xiangtan City Cluster from two aspects of time series dynamics and spatial pattern. Through data analysis, it is found that there is a high correlation between new urbanization and population urbanization rate in Changsha-Zhuzhou-Xiangtan City Cluster. The growth trend of “quality” and “number” of new urbanization is consistent, and the comprehensive level of new urbanization in Changsha-Zhuzhou-Xiangtan City Cluster is constantly improving. At the same time, there are some problems in the new urbanization of Changsha-Zhuzhou-Xiangtan City Cluster, such as unreasonable industrial structure, backward public transport infrastructure, serious structural unemployment, serious air pollution and large gap between urban and rural areas. Finally, the paper puts forward relevant policy suggestions.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".