Urbanization and Economic Growth in China—An Empirical Research Based on VAR Model
Bibliographic record
Abstract
This paper takes the relation between urbanization and economic growth in China as the object of study. By using the time series data ranging from 1982 to 2014 and building VAR model, it analyzes, respectively, the dynamic relations between economic growth and the urbanization rate of resident population, the urbanization rate of land and the quality of urbanization. The paper comes up with the following conclusions: there exists a unidirectional causality between resident population urbanization and China’s economic growth, the former promoting the long-term growth of the latter; unidirectional causality also exists between land urbanization rate and China’s economic growth. However, different from resident population urbanization rate, it is the economic growth of China that promotes the increase of land urbanization rate and the increase of land urbanization rate cannot promote China’s economic growth; the relation between the quality of urbanization and China’s economic growth is a two-way causality. The improvement of urbanization quality has a cumulative positive effect on the economic growth of China, while economic growth has a negative effect on the improvement of urbanization quality in the short term and positive effect on economic growth in the long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".