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Record W4408398643 · doi:10.20525/ijfbs.v14i1.4003

Implementing New-Era Conservation Strategies for Heritage Sites

2025· article· en· W4408398643 on OpenAlexaff
Johannes Johannes, Made Deviani Duaja, Kristanto Januardi

Bibliographic record

VenueInternational Journal of Finance & Banking Studies (2147-4486) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsWorld heritageEnvironmental resource managementEnvironmental planningGeographyEnvironmental scienceArchaeologyTourism

Abstract

fetched live from OpenAlex

This research explores conservation practices at Borobudur, one of Indonesia's prominent heritage sites, and comprehends collaborative conservation strategies that preserve cultural heritage amidst rising tourist pressures. We employ a qualitative approach that incorporates narrative methods, in-depth interviews, triangulation, and observation. Furthermore, we used a thematic examination to achieve a deeper understanding of the data. The findings emphasize the necessity for mutual understanding and collaboration among stakeholders. The study highlights the challenges faced by disadvantaged groups, tour operator firms, pilgrim tourist services, and Taman Wisata Candi (TWC), which have the authority to provide service. So, BKB, the responsible institution, has to encourage parties to collaborate on actions respectively. This study contributes to tourism service theory by offering strategic guidance to mitigate the adverse effects of conservation policies on stakeholders. It underscores the importance of collaborative efforts and tailored strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.109
GPT teacher head0.337
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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