Multi-objective optimization regulation method and intelligent path planning of ideological and political education elements in physical education teaching
Bibliographic record
Abstract
Curriculum Civics reform in physical education should keep pace with the times and actively explore modern technical means.This study addresses the problem of regulating the elements of Civic and political education in physical education, and establishes a mathematical model of multi-objective optimization and regulation by comprehensively considering the various factors and constraints involved in the problem.In order to further optimize the regulation results, an improved two-population genetic algorithm is used to solve the model.Taking the physical education course of a university as an example to analyze, the design algorithm of this paper is compared with the experiments, and the improved two-population genetic algorithm completes the convergence in 300 iterations, and the degree of adaptability is improved by 2.04%, which has the characteristics of strong global search ability and fast convergence speed, which proves that the improved two-population genetic algorithm has a certain degree of superiority and validity.The utilization rate of the elements of ideology and politics education in the experimental solution results reaches 0.87, and other factors meet the actual needs of sports teaching, and the method of this paper can realize the intelligent regulation of the elements of ideology and politics education in sports teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".