Regional Income Disparity and Population Movement: Case Study of Jiangsu Province in China
Bibliographic record
Abstract
This paper discusses the population distribution in a region that needs to solve regional income disparity problems. The targeted region was the Jiangsu Province in China where the intra regional income disparity (city level and prefecture level) shows expansion with recent economic growth. First, the population (population distribution) necessary for regional income disparity to completely disappear under very simple assumptions was estimated. The ‘ desirable’ population was named the “ convergence population” . Differences in the real (estimated) population and the convergence population were compared, and the degree of the solution for income disparity was verified. Consequentially, population cannot be absorbed sufficiently in rich regions, and income disparity is far from being solved. On the other hand, it is possible to think of a population movement plan for the future by estimating the convergence population. The population and accompanying income disparity were estimated in several projections by combining the registered population with the convergence population. In this estimation, a larger adjustment rate produced a greater decrease in the income disparity. Although it is difficult to estimate the regional population including population movement, we hope such discussions will become a concern for policymakers.JEL Classification: O15, O18, O53, R12, R23
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".