Cognition of disaster risk in a tourism community: an agricultural heritage system perspective
Bibliographic record
Abstract
Cognition of risk is the first step in reducing disaster damage and losses. In this study, risk cognition in the Hani Rice Terraces, the core tourism attraction in Yuanyang County, Honghe Prefecture, Yunnan, China, is analyzed based on field survey and participatory geographic information system (GIS). The results show that tourism communities have cognition of risk; are more sensitive to hazards (especially drought); have more severe potential damage and losses from hazards; and also have more enthusiasm to adapt to disaster risk, when compared with a non-tourism community. On disaster vulnerability maps, the tourism communities identified the unique “Forest – Village – Terrace - River” landscape while the non-tourism community only recognized the terrace and the village as the main elements affected by hazard. Also, the tourism communities had deeper understandings of drought, flash floods and landslide disaster risks. A conceptual model based on “Pressure – State – Response” relationships is put forward to explore the situation in which, in the tourism community, terraces have a greater variety of functions and enhanced values resulting in the spatial expansion of hazard effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".