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PENGEMBANGAN AIR TERJUN DLUNDUNG UNTUK MENJADI DESTINASI PARIWISATA UNGGULAN DI KABUPATEN MOJOKERTO

2019· article· en· W2953391054 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Manajemen Pelayanan Hotel · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsSWOT analysisBusinessTourismDocumentationPromotion (chess)MarketingInterviewTroubleshootingGeographyComputer science

Abstract

fetched live from OpenAlex

This aims of the study are finding the development strategy in the tourist area of the Dlundung waterfall to be excellent destination in Mojokerto. This is a descriptive qualitative research. Data collection techniques are observation, interviews, questionnaires, and documentation. Data will be analyzed by SWOT method. Strategies have been found is the increased promotion of the natural beauty of waterfalls and campgrounds, additional facilities of outbound and painball, repair and improvement of facilities, road improvements, additional services and hours of operation of public transport, improving the quality of human resources of tourism, Perhutani reports the condition of the campground to Disparta about prioritized apparatus intensively, Perhutani and Disparta give an opportunity for investors to benefit location of the campsite, ask for the role of local communities in improving the security of tourism, Perhutani maintains the cleanliness and comfort of the facilities at tourist sites, increasing community empowerment in troubleshooting facilities and accessibility.
 Keywords: the development strategy, excellent destination

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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