Research on Deep Learning Algorithm Application and Resource Allocation Optimization in Educational Resources Big Data Analysis
Bibliographic record
Abstract
The equalization and rationalization of educational resource allocation is of great significance to the coordinated development of education.The study takes the educational resources of 13 districts and counties in Y city in 2023 as an example, and proposes to use the BP neural network-based educational resource allocation evaluation system to analyze it.The results show that only three districts and counties have "very good" and "good" levels of educational resource allocation.Accordingly, this paper constructs a multi-objective optimization model to improve the level of educational resource allocation, reduce the differences between counties, and improve the utilization rate of educational resources.The weights corresponding to the eight indicators of the educational resource allocation evaluation index system are solved by the entropy weight method, after which the preset values of the three objective functions and the weights accounted for by the eight indicators are brought into the model and the artificial raindrop algorithm is used to find the optimal solution.After finding the optimal solution of educational resource allocation, the BP neural network-based educational resource allocation evaluation system is used again to evaluate it, and at this time, the educational resource allocation of a total of 12 districts and counties belongs to the "very good" and "good" grades.The study shows that the optimization method of educational resource allocation designed in this paper can reasonably plan educational resources and realize the coordinated development of education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".