Smart Sustainable Ecotourism in Dooars, India: Challenges and Opportunities
Bibliographic record
Abstract
The latest form of "smart tourism" is becoming more and more accepted worldwide.The goal of smart tourism is to provide tourists with technological facilities and integrate them into their travel.Five priority areas have been identified during India G-20 Presidency for accelerating the growth of tourism sector to achieve the targets for the 2030 Sustainable Development Goal.The aim of this research is to determine how technological advancements in ecotourism contribute to the development of sustainable ecotourism in the study area.The study has been done using a literature review, secondary data, and a questionnaire.There are 204 respondents total through random sampling, including tourists, transporters, homestay owners, local shopkeepers, tourist guides, etc., and secondary data collected from the Department of Tourism, Government of West Bengal, Ministry of Tourism, Government of India, census data, etc. Implementation of smart tourism practices through ICT in Dooars region is very difficult due to its remote location and lack of education as well as awareness among the local community.The results of this research show how ICT and other innovative, environmentally friendly technological ideas can be developed for managing and distributing the knowledges for critical decision-making and working of eco-tourism and smart tourism technology simultaneously for improving the tourist experience and the livelihood of local communities.The findings of this research will contribute a better grasp of how strongly stakeholders feel about the need for potential innovative technologies for community development and environmental conservation in Dooars area of West Bengal, India.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".