Study on the Construction of Multilevel Regression Model for the Integration of Sichuan Rural Music and Cultural Tourism Industry under the Rural Revitalization Strategy
Bibliographic record
Abstract
The integration and development of Sichuan's rural music and cultural tourism industry is of great signi icance in the context of rural revitalization strategy.The purpose of this paper is to construct a multilevel regression model to deeply explore the in luencing factors and role mechanisms of the integration of the two.Through theoretical analysis and empirical research, the research variables are clari ied, and the null model, random effect model and complete model are constructed and data validation and analysis are carried out.The results show that the richness of rural music resources, the level of cultural and tourism industry, policy guidance and support, market demand and human resources have a signi icant positive impact on the integration of rural music and cultural and tourism industry in Sichuan.The results of the full multilevel regression model show that the same level of rural music resource abundance has different impacts on the integration of rural music and cultural and tourism industries due to regional differences.The results of the study provide theoretical support for the development of cultural tourism industry in Sichuan Province, and deeply help the implementation of rural revitalization strategy in Sichuan Province.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".