Multiple Drivers Influencing Residents’ Perception of Ecotourism in a Biodiversity Rich Forest Protected Area of Bangladesh
Bibliographic record
Abstract
Local people have both positive and negative attitudes towards ecotourism. It is because they are the beneficiaries of ecotourism, though they are sometimes the victims of its activities. Expression of the residents’ perception depends on multiple drivers. This study assesses the drivers influencing local peoples’ perception of the impacts of ecotourism in Satchari National Park (SNP) - a biodiversity rich forest protected area and famous ecotourism spot in Bangladesh. Interview surveys on local people of purposely selected four villages in and around SNP supplemented by the questionnaire were conducted from September to October 2022. This study reveals that socio-cultural aspects secured higher ranks by processing higher mean values, which follow economic and environmental elements. It was also found that local people’s attitudes towards ecotourism vary with the variation in education, occupation, and income. Policymakers and forest department officials should take the necessary actions to solve the negative impacts of ecotourism. The negative impacts of ecotourism were ‘increased noise pollution and waste’ and ‘overcrowding’.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".