Optimizing Physical Education Resource Allocation Using Principal Component Analysis
Bibliographic record
Abstract
Physical education teaching resources are an important part of teaching resources, and it is necessary to adopt a sustainable development approach to ensure the rational utilization of resources.In this paper, firstly, the factors affecting the allocation of physical education teaching resources in colleges and universities are analyzed by using principal component analysis and systematic cluster analysis, and the validity of the method is verified.Secondly, it constructs the influential element model of regional physical education teaching resources allocation efficiency level based on Tobit regression, and explores the locational factors affecting the distribution of physical education teaching resources.Finally, relevant countermeasure suggestions were put forward based on the analysis results.Using principal component analysis to downscale the 17 indicators of the influencing elements of physical education teaching resource allocation in the statistical data, four principal components were obtained, whose cumulative contribution rate was as high as 90.22%, which was greater than 85%, i.e., it had a 90.22% degree of explanation for the original data.Then, the dimensionality-decreased data were clustered and realized to evaluate and rank the allocation of physical education teaching resources in 23 sample universities.In addition, the results of Tobit multiple regression analysis showed that factors such as regional geographic location, regional population density, regional economic development and the scale of investment in physical education teaching resources all have different degrees of influence on the allocation efficiency of regional physical education teaching resources.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".