A Study on Optimization of Rural Public Facility Layout Based on Mixed Integer Planning and Strategies for Improving Social Governance Effectiveness in the Era of Big Data
Bibliographic record
Abstract
Since the strategic plan for rural revitalization was put forward, the related contents of public facilities have been continuously written into national policies.Promoting the high-quality construction of rural public facilities has become a hot topic of research in China's rural areas.In this paper, optimization ideas and frameworks are proposed for the layout of rural public service facilities.Using the mixed integer planning model, the optimal solution of facility layout is obtained by calculating the distance between facilities to realize the optimization of rural public facility layout.Moran's I index in global spatial autocorrelation is used to analyze the degree of spatial autocorrelation of rural public basic facilities accessibility.Construct a multivariate linear model to assess the impact of mixed integer planning applications on rural residents' sense of social governance effectiveness.Evaluating the efficiency of rural basic public service facility accessibility coverage, the number of rural clinics is much larger than other facilities, with 26 facilities, and the number of middle schools, township general hospitals, and post offices is smaller, all with only two, indicating that there are certain problems in the configuration and spatial layout of public service facilities in a certain rural area nowadays.The application of the mixed integer planning model has a significant impact on rural governance in terms of human development index, public services, social security, public safety and social participation, with regression results of 0.075, 0.068, 0.125, 0.083 and 0.164, respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".