Intergovernmental Cooperation in Cheng-Yu Economic Zone: A Case Study on Chinese Regional Collaboration under Synergy Governance
Bibliographic record
Abstract
Intergovernmental collaboration is a universal trend with regional integration. What the local governments, as the public sector, should do to effectively respond to this trend, to strengthen the regional cooperation, transform local governmental functions to improve administrative performance and propel the regional development, a series of such questions, have aroused widespread concern. Cheng-Yu economic zone in the southwest of China, playing an important role in the economic development of this region, is faced with increased demand for addressing regional public issues and promoting regional synergic development. However the intergovernmental cooperation in Cheng-Yu economic zone is complex with fruits accompanied by problems, which makes the study of corresponding countermeasures feasible and necessary.On the basis of synergy governance theory and other relevant theory on intergovernmental cooperation, the paper builds a theoretical framework to analyze regional intergovernmental collaboration. Then considering the current condition of collaboration between and among local governments in Cheng-Yu economic zone, the paper leads a case study, elaborating on the problems lying in the cooperation in this zone and the corresponding causes. Then with the internal and external environmental analyses, the paper designs a brief conception on propelling synergic development in this zone.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".