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Record W2163766009 · doi:10.5539/ass.v9n2p203

Involvement in Agro-Tourism Activities among Communities in Desa Wawasan Nelayan Villages on the East Coast of Malaysia

2013· article· en· W2163766009 on OpenAlexvenueno aff
Zaim Fahmi, Azimi Hamzah, Mahazan Muhammad, Sulaiman Md. Yassin, Bahaman Abu Samah, Jeffrey Lawrence D Silva, Hayrol Azril Mohamed Shaffril

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsTourismGovernment (linguistics)FishingEconomic growthPovertyGeographyAgricultureSocioeconomicsBusinessFisherySociologyEconomics

Abstract

fetched live from OpenAlex

Over the years, most developing countries across the globe have identified agro-tourism activities as being able to assist their respective country’s development. Malaysia is no exception to this; the country has emphasized agro-tourism as a significant tool of development and poverty eradication, and perhaps, at the same time, a key strategy by which to address the problem of rural migration. The Fisheries Development Authority of Malaysia, also known as LKIM, has been playing a part in responding to the government’s call to develop the agro-tourism industry in Malaysia. LKIM has taken steps towards formulating an agro-tourism program for fishing communities called Desa Wawasan Nelayan. The purpose of this paper is to determine the level of involvement in agro-tourism amongst fishing communities in the east coast region of Malaysia. The study was conducted amongst 220 respondents from two villages in Terengganu and Kelantan. The findings of the research reveal that despite the government’s intervention in promoting agro-tourism activities throughout the nation, the level of involvement amongst respondents is still at a moderate level.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.035
GPT teacher head0.298
Teacher spread0.264 · 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 designQualitative
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

Citations15
Published2013
Admission routes1
Has abstractyes

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