Economic Development and Regional Balance Research in Yunnan Province Since 1992
Bibliographic record
Abstract
Based on compound average growth rate, relative development index, Gini coefficient regional development balance and R/S analysis method, this paper selects three indicators and uses 16 regions of Yunnan Province as analysis units to research regional economic development level and its future trend. Quantitative analysis results show that: 1) Per capita GDP, rural per capita net income and average salary of employee increase steadily, but the income gap between urban and rural areas is enlarging. 2) Among the 16 regions of Yunnan Province, the difference of Per capita GDP is great and the differences of rural per capita net income and the average salary of employee are small. 3)Among the 16 regions of Yunnan Province, the per capita GDP over the balance degree is low but shows obvious upward trend, rural per capita net income balance degree is high and rising, average salary of employee is the highest overall balance but decreased slowly. 4) Regional differences of GDP per capita and rural per capita net income will be further narrowed, and regional differences of the average salary of employees will increase slowly. This paper may help lay a foundation for further studies on the relationship between fairness and efficiency to balance the regional economic development and give an objective understanding of economic development of Yunnan Province.
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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 | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".