Computational Analysis of Resource Allocation Optimization and Dynamic Planning for Continuing Education under Community Education Governance Framework
Bibliographic record
Abstract
In this paper, Kernel density estimation method is used to analyze the distribution characteristics of continuing education resources and reveal the distribution pattern of resources in different communities.On this basis, CCR model and BCC model are introduced to optimize the DEA model of data envelopment analysis and evaluate the resource allocation of continuing education institutions.The resource allocation optimization and dynamic planning system of continuing education is further constructed, and the system dynamics simulation method is used to simulate the optimization process of resource allocation, which provides a scientific basis for the governance of community education.The results show that: continuing education resource input is polarized in quantity, its performance level is not high, regional differences are significant, and scale efficiency is a key factor restricting quality improvement.This paper constructs a system dynamics model for the quality and user use of educational information resources, and in view of the difficulties of optimization and dynamic planning of the allocation of continuing education information resources, it is proposed that the managerial and digital educational resource platform construction-based inputs such as teachers' information technology application ability, assessment system construction, etc. should be improved to promote the high-quality and balanced development of continuing education informatization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".