The Way of Regional Economic Development by Cooperating Activities of Xinjiang and Other Provinces of China
Bibliographic record
Abstract
This paper focuses on the cooperation efficiency between Xinjiang Uygru Autonomous Region (XUAR) and the western provinces of China in the first stage (1999-2011) to estimate the cooperation possibility for next period and the optimal way of cooperation process. This paper analyzed and compared the main macroeconomic indicators of XUAR and the western provinces, by using the macroeconomic data of China and 12 western provinces from 1999 to 2011. The correlation coefficient proved that the economic development correlates highly of XUAR and the western provinces in the period from 1999 to 2011. This paper also compared the main economic indices, such as GDP, growth rate and so on, to know the status of economic condition of each province. The result proves that the cooperation activities between XUAR and the western provinces not only improved the XUAR’s economy but also provided more finances chances for the western provinces, and the cooperation was successful in the first phase. However, this research found the some issues existed in the process of cooperation. To solve these questions in next cooperation, this paper pointed out several main problems, including the lack of the investment in fixed assets and foreign capital, readjusting industrial structure, reducing the residents’ income gap between XUAR and the western provinces and nationwide as well.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".