Establishing tourism sustainability in a globally important agricultural heritage system in China: A case of social and eco‐system recovery
Bibliographic record
Abstract
Abstract This paper in the form of a narrative, contributes an account of revitalizing sustainability of a World Heritage site—the Hani rice‐fish terrace system—in a pristine area of cultural and ecological significance in Southwest China. Rice‐fish farming in mountain terraces is an ingenious system that has existed in various forms for hundreds of years. However, due to international recognition by FAO and UNESCO of this area as a world agricultural heritage site, the system was threatened of losing its eco‐social balance because of mass tourism overload. Remarkably, after a period of development chaos and because of changing actors and roles, a semblance of sustainability has been regained through firm protection and management of the chief tourism asset—the rice terraces. Utilising a stakeholder approach to form multiple narratives, our research reveals the interplay of government, private enterprise, Hani people and outside experts in reshaping the trajectory of tourism development when the world‐heritage attractiveness brings new threats to the ecological systems and turned the destination into a challenging geographical environment. It demonstrates the need for strong and inclusive management techniques some of which have now been applied in the core Hani area, with appreciable success.
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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 | 0.002 | 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 | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".