Sustainability Initiatives for Green Tourism Development: The Case of Wayanad, India
Bibliographic record
Abstract
Mainstream sustainability discussions draw focus to the balance between commercial and environmental interests. Responsible tourism (RT) practices are an outcome of these discussions and RT is a significant contributor to the “greenification” of economies in many countries. Green tourism promotes travel that supports natural and cultural aspirations, while also supporting protection of the destination community’s limited resources. Kerala, India, is a pioneer in implementing RT. The present study exploratively analyzes the RT initiatives at different phases, especially within the lens of sustainable responsible tourism initiatives for green tourism development. The research is descriptive in nature and is guided by the bottom line approach (TBL) for green economic development. The findings highlight the dynamics of challenges experienced in the different phases of RT implementation. Based on our analysis of the secondary data, the first phase implementation of RT was not very successful; the second and the third phases seemed to be more promising. The study also throws light on the need for future studies in other culturally distant destinations; this will result in promising practices being adopted as alternative strategies for sustainable tourism development globally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".