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Record W4413706157 · doi:10.18280/ijsdp.200727

Tourism Potential Shaped by the Morphology of the Historic City of Lasem in Central Java, Indonesia

2025· article· en· W4413706157 on OpenAlexvenueno aff
Mutiawati Mandaka, Wiendu Nuryanti, Dyah Titisari Widyastuti

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsnot available
FundersUniversitas Gadjah Mada
KeywordsJavaTourismGeographyEnvironmental planningEnvironmental resource managementBusinessEnvironmental scienceComputer scienceArchaeology

Abstract

fetched live from OpenAlex

Lasem, a historic city in Central Java, Indonesia, possesses a unique urban morphology shaped by its rich cultural heritage and historical development.The city's distinctive layout and architectural elements reflect influences from Chinese, Javanese, and colonial traditions.Understanding how Lasem's urban morphology contributes to tourism potential is essential for sustainable heritage preservation and economic growth.This study employs a qualitative approach, combining field observations, spatial analysis, and interviews with local stakeholders.Historical documentation and urban morphology analysis are used to examine the city's structural patterns, heritage zones, and tourism dynamics.Lasem's unique urban morphology demonstrates significant tourism potential throughout its development periods.Findings indicate that the city's urban morphology across seven historical layers forms three classifications of tourism potential: existing tourism potential, developing tourism potential, and ideal tourism potential.These tourism potentials are derived from physical urban morphological elements, the distribution of physical elements, historical urban components, historical city patterns, and tourism targets (attractions, amenities, and accessibility).

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.268

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.269
Teacher spread0.258 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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