Design and Application of an Evaluation Index System for Urban Development Quality of China’s Sub-provincial Cities in the New Era
Bibliographic record
Abstract
China has entered a new era of development. In the new era, the quality of urban development is endowed with new connotations. Through in-depth analysis on these connotations, this paper sets up a 1+2+3+4+5+6 logical framework and then establishes an evaluation index system for urban development quality in the new era. After that, the analytic hierarchy process (AHP) and weighted sum model (WSM) were introduced to evaluate the development quality of the 15 sub-provincial cities in China, based on the statistics released by the state and the cities in 2017. The results show obvious regional differences in the quality of urban development. In general, the cities in the eastern region are more developed than those in the central and western regions, and the cities in the southern region are more developed than those in the northern region. The cities in developed areas boast relatively high development quality, because city clusters are relatively mature in these areas. Besides, large cities are not necessarily better developed, i.e. there is no absolutely positive correlation between city size and development quality. In addition, there are marked differences between the sub-provincial cities in culture and urban management. To realize high-quality development, a city must strike a balance between multiple aspects, such as economy, society, ecology, public service and urban management, according to their own features. The research results shed important new light on urban development in the new era.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".