The Impact of Rural Tourism on Rural Culture Evidence from China
Bibliographic record
Abstract
The development of rural tourism plays an important role in promoting rural culture. By integrating 3833 household questionnaires from the 2020 China Rural Revitalization Survey (CRRS) database with remote sensing data, we constructed an evaluation system to measure the level of rural culture. Then, we analyzed the impacts of rural tourism on rural culture from macro and micro perspectives. Our research results show the following: (1) Villages with developed rural tourism show a 85.9% increase in rural culture compared to those without tourism; (2) mechanism tests show that rural tourism promotes the rural culture by improving households’ risk-sharing behavior, human resources, and self-identification, leading to increases of 3.4%, 55% and 10.9%, respectively; (3) with micro-level (fieldwork survey) and macro-level analysis (remote sensing), we analyzed the various impacts of rural tourism on rural culture under different income levels, demographic structures, geographical locations and topographical conditions. The results show that at the micro level, the promotion effect of rural tourism on rural culture increases by 2.214% and 1.679% with the increase in per capita income and the proportion of women, respectively. For geographical location, macro-level data suggest that rural tourism in the east of China increases the rural culture by 3.416%. Moreover, in plain areas, both micro- and macro-level analysis indicated that rural tourism promotes rural culture by 2.323% and 4.607%, respectively. This is the first time rural culture has been evaluated on a large scale with two cross-validated approaches.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".