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Record W4412942407 · doi:10.56975/ijsdr.v10i7.301559

Community Based Ecotourism and Community Development in East Khasi Hills District Meghalaya

2025· article· en· W4412942407 on OpenAlexfundno aff
Martius Rangiasaid Rynjah, Grace Lalhlupuii Sailo

Bibliographic record

VenueInternational Journal of Scientific Development and Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
FundersInstitute of Aboriginal Peoples HealthBiodesign Institute, Arizona State University
KeywordsKhasiEcotourismGeographyEnvironmental planningTourismSocioeconomicsArchaeologySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.384
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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