Evaluation and prediction of high-quality development in China: A time-spatial analysis from Hubei province
Bibliographic record
Abstract
High-quality sustainable development is the common goal pursued by all countries in the world. China’s high-quality development (HQD) includes five concepts of “innovation, coordination, green, opening-up, and sharing”. In this context, we established an evaluation system that included these five fundamental characteristics, used the comprehensive entropy method and BP neural network to evaluate and predict the high-quality development of Hubei Province in China, and conducted a spatiotemporal deductive analysis. The study found that: 1) Economic growth still has an important impact on HQD, for all the five main indicators, “opening-up” and “innovation” have the highest impact weights, which are 0.379 and 0.278, respectively, while the proportions of coordination and sharing are both less than 0.1. 2) There are huge differences in the level of high-quality development between regions in Hubei Province. From 2010 to 2020, the average comprehensive index of Wuhan City was greater than 0.5, which is 7 times that of the second Xiangyang City, and 46 times that of the last Shennongjia district. 3) In the past few years, the overall high-quality development of Hubei Province has shown a fluctuating upward trend. However, due to the impact of COVID-19, during the following years, its comprehensive development index will decline by an average of 5% annually, but starting from 2022, it will gradually increase. As a result, tailored and coordinated sustainable environmental policies of integrating institutional and open-market measures should be provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".