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Record W4210725128 · doi:10.3390/jrfm15020052

Sustainability Initiatives for Green Tourism Development: The Case of Wayanad, India

2022· article· en· W4210725128 on OpenAlexvenueno aff
Nimi Markose, Bindu Vazhakkatte Thazhathethil, Babu George

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSustainabilityMainstreamSustainable developmentSustainable tourismEcotourismBusinessNatural resourceDestinationsFocus groupEnvironmental planningEconomic growthPolitical scienceEnvironmental resource managementMarketingGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations31
Published2022
Admission routes1
Has abstractyes

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