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Record W4404635095 · doi:10.3390/jrfm17120529

From Traditional-Ritual Activities to Financial Report: Integrating Local Wisdom in Bantengan Financial Bookkeeping

2024· article· en· W4404635095 on OpenAlexvenueno aff
Ana Sopanah, Adya Hermawati, Syamsul Bahri, Imanita Septian Rusdianti

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsBookkeepingAccountabilityThe artsAccountingDocumentationContext (archaeology)Transparency (behavior)SociologyPublic relationsBusinessPolitical scienceGeography

Abstract

fetched live from OpenAlex

This study examined the integration of cultural accounting in the conservation of a traditional performing art called Bantengan in Malang Raya, Indonesia, that is rich in local wisdom and spiritual values. The study focused on exploring the values of local wisdom contained in Bantengan and analyzing accounting records in its financing, especially post-COVID-19 pandemic. Using a qualitative approach with an ethnomethodological paradigm, data were collected through observation, in-depth interviews, and documentation from the Sukopuro Bantengan Association. This study revealed the importance of accountability in the management and conservation of traditional arts to ensure transparency, sustainability, and relevance of cultural values in an ever-evolving social context. Accounting, often associated with technical aspects, in this context also reflects humanistic and cultural values. The findings of this study are expected to provide a new perspective in the field of cultural accounting, especially related to the conservation and development of traditional arts in Indonesia, as well as provide a useful framework for the management of cultural assets in other regions that have similar contexts.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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 designQualitative
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

Citations5
Published2024
Admission routes1
Has abstractyes

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