Framework for Building Knowledge Map of Ethnic Music Based on Big Data
Bibliographic record
Abstract
National music is a cultural treasure with unique charm and charm in the traditional Chinese culture, which has high research value and broad social influence. This paper aimed to explore the construction of ethnic music knowledge map based on big data analysis. This paper proposed the knowledge map of ethnic music and big data clustering analysis, and studied the experimental results of constructing the knowledge map of ethnic music based on this research. The experimental results of this paper showed that the knowledge map provided a scientific framework and method for the exploration of ethnic music knowledge. It made a reasonable explanation and evaluation for researchers in the field of ethnic music and pointed out the direction for the development of ethnic music. This paper identified 24 high-frequency keywords, which are the basis of co-word analysis. Among them, "national music" appeared 1940 times, and "ethnomusicology" appeared 465 times. "Folk music" appeared 415 times, and "music tradition" appeared 276 times. "Chinese music" appeared 270 times. Multivariate statistical methods are often used in co word analysis. These are the central links in co word analysis. Clustering analysis was used to classify keywords in ethnic music, thus revealing the current hot topics in ethnic music. In a word, the construction framework of ethnic music knowledge map based on big data analysis is conducive to the development of ethnic music.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".