Potential Belum Temenggor Forest Complex as a most popular ecotourism destination in Perak / Mohd Asyraf Bakhrurazi
Bibliographic record
Abstract
Tourism is a sector made up of many subcategories, such as nature tourism, agro-tourism, anthro-tourism, safari tourism, academic tourism, wilderness tourism and more (Leung, 2015). Community-based ecotourism is one faction of tourism that challenges many aspects of the mainstream, mass tourism. Despite its roots in the early 1980’s, a definition for ecotourism agreed upon by all has yet to be found, although there are some frequently used in ecotourism literature. Two frequently cited definitions are put forward by the International Ecotourism Society and the Quebec Declaration of Ecotourism. The International Ecotourism Society defines ecotourism as “responsible travel to natural areas that conserves the environment and improves the well-being of local people” (Fletcher, 2015). The definition of ecotourism stems from the division of ecotourism into two categories: hard and soft ecotourism. Hard ecotourism is categorized as more active and catering to small groups with few services and comforts provided (Singh, Slotkin and Vamosi, 2007). It caters to visitors with a solid knowledge of conservation looking for longer stays and seeking more specialized trips and activities (Eagles, McCool and Haynes, 2002; Singh, Slotkin and Vamosi, 2007). Hard ecotourism is typically associated with areas not easily accessed through conventional tourism, such as coastal reefs and mountainous regions (Duffy, 2002). Soft ecotourism, on the other hand, promotes more convenience and comfort for the tourists who prefer shorter stays and more outside services to ensure their comfort (Singh, Slotkin and Vamosi, 2007).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".