Community Based Ecotourism and Community Development in East Khasi Hills District Meghalaya
Bibliographic record
Abstract
This paper seeks to provide a glimpse of the practice of community-based ecotourism is few selected ecotourism destinations in East Khasi Hills District, Meghalaya. Ecotourism development in the villages of North East India has been driven by a combination of socio-economic, environmental, and strategic factors. The region's rich biodiversity and scenic landscapes, have made it a prime location for ecotourism. Tourism in Meghalaya has a very long history and can be dated back to the colonial period when the colonial rulers made Shillong a hill station capital as a retreat from the scorching heat and humid summers. Meghalaya is blessed with rich flora-fauna and biodiversity that has a potential of being one of the hotspots of ecotourism in the northeast India. The findings revealed that, in the context of Meghalaya, ecotourism has been initiated through and community-based initiative emphasising on the active participation and contribution of members of the host communities in its growth and progress. Community-based ecotourism in the selected villages has brought about social and economic benefits to the households and to the community as well. The practice of community-based approach to ecotourism management ensures active participation of the members of the host communities in the process of conservation and sharing of benefits.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".