A Machine Learning Algorithm-Based Quality Improvement Strategy for Inclusive Preschool Public Services
Bibliographic record
Abstract
Based on the relevant theoretical basis and research experience, this paper constructs a three-in-one, subjective and objective evaluation index system of inclusive preschool public service quality of "flexible", "green" and "soft" quality.Subsequently, the GA-BP neural network quality assessment model based on machine learning algorithm was constructed by utilizing BP neural network analysis and hierarchical analysis to assign weights to the indicators.It was applied in a scientific operation process to synthesize subjective and objective data to understand the quality of public services of inclusive preschool education, and to propose an improvement path in combination with the IPA analysis model.The results show that the weights of the three first-level indicators are 0.428, 0.4231 and 0.1489, respectively, and the weights of the four second-level indicators, including special child care and migrant child care, are more than 0.1, while the weights of the other second-level indicators are all less than 0.1.Among the third-level indicators, the weights of the reasonable sharing of the cost of pre-school education among the government, families, kindergartens and the society, and the synergy of education and rehabilitation are more than 0.05, while the weights of the other third-level indicators are all less than 0.5, and the weights of the other third-level indicators are more than 0.1.In addition, the difference between the actual evaluation results and the simulation evaluation results of the teaching quality of universal preschool education public services is relatively small.And the error between the real values of GA-BP model is extremely small, and its average error is only 0.483.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".