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Record W4414996524 · doi:10.47492/jih.v14i1.3843

PEMBERDAYAAN MASYARAKAT MELALUI PENGEMBANGAN DESA WISATA (STUDI KASUS: DESA TETEBATU, KECAMATAN SIKUR, LOMBOK TIMUR)

2025· article· id· W4414996524 on OpenAlexaff
Teguh Satriawan, I Made Murdana

Bibliographic record

VenueJurnal Ilmiah Hospitality · 2025
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
Keywordsnot available

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk mengeksplorasi bentuk pemberdayaan masyarakat dalam pengembangan Desa Wisata Tetebatu. Penelitian ini menggunakan metode deskriptif dengan pendekatan kualitatif, teknik pengumpulan data dalam penelitian ini dilakukan dengan observasi, wawancara, dan dokumentasi. Kemudian setelah data terkumpul baik itu data primer dan sekunder, selanjutnya akan dilakukan analisis data menggunakan teknik analisis data model Miles dan Huberman yang terdiri dari data reduction (reduksi data), data display (penyajian data), dan conclusion drawing / verification (penarikan kesimpulan / verifikasi). Hasil penelitian menunjukkan bahwa pemberdayaan masyarakat dalam pengembangan Desa Wisata Tetebatu dilakukan melalui berbagai bentuk kegiatan seperti pendampingan yang terdiri dari (pelatihan bahasa inggris, pelatihan tata kelola homestay, pelatihan pengolahan sampah (ecobrick), pelatihan tour guide dan porter, dan pelatihan untuk membuka kelas memasak (cooking class)), pembangunan sarana dan prasarana, serta pembentukan organisasi desa wisata yaitu Kelompok Sadar Wisata (Pokdarwis). Hal ini turut berperan penting dalam memberikan kontribusi besar pada peningkatan partisipasi dan kesejahteraan masyarakat setempat. Dengan pemberdayaan ini, masyarakat dapat memperoleh kemandirian ekonomi, melestarikan lingkungan, dan memperkuat budaya lokal. Pada akhirnya, hal ini akan mendorong pariwisata berkelanjutan yang berbasiskan masyarakat.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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