Research on Digital Educational Resource Management System of China-ASEAN Folklore Sports Culture Based on Knowledge Graph
Bibliographic record
Abstract
With the rapid development of digital technology, the inheritance and dissemination of folklore sports culture have ushered in new opportunities and challenges.This paper constructs a digital educational resource management platform for China-ASEAN folklore sports culture based on Knowledge Graph.The knowledge system of folklore sports culture is systematically constructed by using Knowledge Graph, the data related to China-ASEAN folklore sports culture are collected and organized, and the construction of the corpus of China-ASEAN folklore sports culture domain is completed.Then we extracted knowledge from the data of folklore sports culture domain and stored the obtained knowledge in Neo4j graph database.The China-ASEAN Folklore Sports Culture Digital Education Resource Management Platform, which includes several modules such as login and registration, courses, personal center, institutions and teachers, and backstage management, was designed.The construction and application of the platform gained 91.2% satisfaction from students, enhanced students' interest in learning folklore sports culture, helped to protect and pass on the rich China-ASEAN folklore sports cultural heritage, and also promoted in-depth exchanges and communication between the two sides in the field of sports and culture to build a community of human destiny.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".