An Optimization Model for Spatial Allocation of Compulsory Education Resources in Guangxi Townships
Bibliographic record
Abstract
The spatial allocation of compulsory education resources in rural townships faces significant challenges that affect educational equity, quality, and accessibility. China’s rapid urbanization and rural revitalization strategies have intensified demands for optimized educational resource distribution while existing allocation systems remain inadequate. This research aimed to: (1) identify and validate essential components of spatial allocation of compulsory education resources in Guangxi townships, (2) assess current and desired states of resource allocation across seven key components, and (3) develop a comprehensive optimization model based on educational equity principles and systematic resource management. A three-phase sequential mixed-methods design was employed. Phase 1 validated seven resource allocation components through expert consultation (n=5). Phase 2 assessed current and desired states using surveys with 438 stakeholders from township schools. Phase 3 developed and validated an optimization model incorporating systematic needs analysis, strategic allocation planning, and continuous productivity monitoring. Seven primary resource components were identified: Material Resources, Financial Resources, Human Resources, Policy Support and Management Systems, Curriculum Resources, Social and Community Resources, and Technological and Informational Resources. Significant gaps existed between current allocation levels (X̅=3.48, medium level) and desired allocation levels (X̅=4.55, very high level), with priority needs index ranging from 0.207 to 0.254 across all resource components. Expert validation confirmed very high model suitability (X̅=4.65) and feasibility (X̅=4.20). This study provides the first comprehensive framework for optimizing spatial allocation of compulsory education resources in Chinese rural townships. The developed model offers a systematic, evidence-based approach combining equity principles with practical implementation strategies, demonstrating high suitability and feasibility for policy implementation.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".