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Record W1938600738 · doi:10.5539/mas.v9n8p399

Local Community and Tourism Development: A Study of Rural Mountainous Destinations

2015· article· en· W1938600738 on OpenAlexvenueno aff
Mastura Jaafar, Norjanah Mohd Bakri, S. Mostafa Rasoolimanesh

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersMinistry of Higher Education, Malaysia
KeywordsTourismPanacea (medicine)Rural tourismLocal communityDestinationsNational parkGeographyCommunity developmentEconomic growthRural areaSocioeconomicsLocal economic developmentNatural resourceEnvironmental planningTourism geographyBusinessPolitical scienceSociologyMedicine

Abstract

fetched live from OpenAlex

Malaysia is a popular rural destination worldwide because of its natural heritage. Rural tourism is increasingly viewed as a panacea, increasing the economic viability of marginalized areas, stimulating social regeneration, and improving the living conditions of rural communities. This study explores local community involvement in rural tourism development in Kinabalu National Park, Sabah. This study aims to obtain the local community’s perception of their involvement in tourism development and to determine the benefit of tourism destination development. To address these objectives, the quantitative method was employed. A total of 378 questionnaires were returned by the respondents. The research was conducted in the villages surrounding Kinabalu National Park. Residents have positive perceptions of tourism development in the area. Local communities prefer to be involved in the tourism sector because it improves their key income resources and quality of life.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.347
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations37
Published2015
Admission routes1
Has abstractyes

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