Sustainable tourism and its use as a development strategy in Cambodia: a systematic literature review
Bibliographic record
Abstract
Governments in many developing countries endorse sustainable tourism, despite associated practical limitations and challenges as a development strategy. Using the case of Cambodia, this paper illustrates how sustainable tourism issues in a developing country may be better understood through a systematic review of scattered relevant peer-reviewed and grey literature. This can help in identifying emergent themes and challenges, gaps in knowledge, and opportunities for future research. Analysis of 77 documents on Cambodia's sustainable tourism revealed themes and challenges relating to: (1) an emphasis on nature-based tourism (ecotourism); (2) calls for greater community engagement for sustainable outcomes; (3) stakeholder perceptions and values as drivers of tourism success; (4) cultural heritage as a key tourism attraction; and (5) foreign investment shaping Cambodia's tourism future. Unequal emphasis is given to key government policies for tourism development and there is limited practical guidance on how to realise a vision of sustainable tourism. There is a lack of consideration of how the socio-economic and cultural context affects sustainable tourism. The evidence suggests that sustainable tourism in Cambodia is questionable until fundamental economic, social capacity and policy issues are addressed, along with greater emphasis given to the tourism system's demand side.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".