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Record W3081462138 · doi:10.24043/isj.124

Tourism, accommodation, and the regional economy in Indonesia’s West Papua

2020· article· en· W3081462138 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAccommodationTourismVisitor patternEconomicsIndigenousEconomic impact analysisInput–output modelEconomyBusinessGeographyAgricultural economicsMarket economy

Abstract

fetched live from OpenAlex

This study deals with the contribution of visitor expenditure on West Papua’s regional economy. It accomplishes three objectives: (1) to estimate the economic contribution of domestic and inbound visitor expenditure; (2) to measure the economic contribution of tourist spending at various accommodation classes; and (3) to describe the use of local commodities and labor in the regional accommodation industry. To accomplish the first and second objectives, an input-output multiplier analysis was employed. As for the third objective, interviews were conducted with 35 representatives from regional accommodation establishments. Tourism is found to contribute greatly to the regional economy, as shown from the higher overall output multiplier for tourist expenditure as compared to the regional output multiplier. The output multiplier for inbound tourist expenditure is higher than the domestic tourist. Three-star accommodations are found to be the biggest contributor with outstanding inter-sectoral impact on fisheries; food, beverage, and tobacco manufacture; and agriculture. The qualitative analysis suggests the existence of a large leakage (±90%), mainly in produce and chemicals used in daily operations. Fisheries and wood furniture are the exception. Overall, the accommodation sector absorbs a considerable extent of local labor (73%), 23% of which are Indigenous Papuans.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.979

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.316
Teacher spread0.268 · 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