Ancient town tourism and the community supported entrance fee avoidance – Xitang Ancient Town of China
Bibliographic record
Abstract
Ancient towns, embedded with traditional architecture, culture and life style, are popular tourism attractions worldwide. Tourism use often transforms residents’ living spaces into shared multi-functional spaces for residents and tourists, which imposes impacts to residents and complicates community and tourism relationships. Entrance fees are widely used as an economic strategy for destination management and benefit re-distribution, triggering changes in relationships with profound implications socially and culturally. A framework is proposed to map relationships between stakeholders and flows of tourism impacts among key stakeholders in ancient town tourism. Readily accessible to a large urban population in the eastern developed area of China, Xitang Ancient Town in Jiaxing City of Zhejiang Province is one of the most famous tourism ancient towns in China. A mixed-methods approach involving both onsite and offsite data collections is used to explore and explain the enduring community support for the avoidance of entrance fee payments by tourists at Xitang Ancient Town. Limited access to tourism benefits for residents adjacent to the ticketing area is identified as the underlying reason for this. Practical suggestions are made to enhance community participation and ensure equitable access to tourism benefits for Xitang and ancient towns in China and elsewhere.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".