Analysis of the Influencing Factors of Farmers’ Willingness to Participate in the Integration of Tea and Tourism—An Empirical Study of Tea Farmers in Emeishan City
Bibliographic record
Abstract
This paper is based on a questionnaire survey of 277 tea farmers and the operating enterprises of Jia’e Tea Valley in Emeishan City. Using the theory of deconstructive planning behavior, the influencing factors of the willingness of tea farmers to participate in the integration of tea and tourism are analyzed. In order to unleash the enthusiasm and initiative of tea farmers, promote the organic integration of tea industry and tourism, increase their income, and promote rural revitalization. The results indicate that (1) Behavioral attitudes, subjective norms, and perceived behavioral control have a significant impact on the willingness of tea farmers to participate in the integration of tea and tourism, but the influence of tea tourism integration concepts on tea farmers’ behavioral attitudes is not significant. (2) Individual characteristics such as age, education level, family labor force, and per capita disposable income have a significant impact on tea farmer behavior attitudes, subjective norms, and perceived behavioral control, and have a significant indirect impact on the willingness to participate in tea tourism integration. Therefore, this article suggests that grassroots organizations should play a guiding role and increase publicity on the necessity and feasibility of integrating tea and tourism; Give full play to the leading role of village and community cadres, as well as the exemplary and driving role of tea farmers in the same village; Efforts will be made to enhance the recognition and trust of tea farmers in the enterprises they rely on, increase revenue expectations, expand participation channels, reduce participation thresholds and risks, and make tea farmers feel that they can make progress and gain through tea tourism integration, thereby enhancing their enthusiasm and initiative in participating in tea tourism integration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".