Ecotourism Impacts on the Economy, Society and Environment of Thailand
Bibliographic record
Abstract
During the past decade, there have been a number of ecotourism studies in various disciplines to provide a knowledge foundation for sustainable tourism development. However, most prior studies have examined the contributions of the ecotourism destinations in the economic and/or environmental dimensions. Little research has investigated the contributions of the ecotourism businesses in terms of business practices and their products to the three dimensions of sustainable development. This paper examines how ecotourism tour operators and their guided tours contribute to the development of economic, social and environmental dimensions at ecotourism sites and local communities. Data were collected from ecotourism tour operators through the interview and observation methods, and the contents were analyzed in accordance with ecotourism concepts and principles. The paper reveals that the practices of tour operators and their guided tours contributed economic, social, and environmental benefits to the ecotourism destinations and local communities. Interestingly, this paper finds that the length (duration) and types of guided tours had different contributions and impacts on the three dimensions of sustainability. In particular, guided tours with a local visit contribute greater economic and social benefits to the local areas than tours without a local visit. Recommendations are provided to promote responsible ecotourism business.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".