Bayesian Network approach in analyzing the sustainability of the cultural industry ‘the sacred’ Gringsing Weaving
Bibliographic record
Abstract
Bali is a popular tourist destination in the world, and the main entry point for foreign tourists to Indonesia. Bali is also an area in Indonesia that is famous for producing woven fabrics, which have various characteristics in each region. Gringsing weaving is one of the traditional Balinese fabrics from Tenganan Village which is considered sacred and its manufacture takes quite a long time, and is carried out with special techniques that are very difficult. This study aims to analyze and map factors related to the sustainability of the Gringsing weaving cultural industry, using the Bayesian Network approach. The results of the FGD with related stakeholders mapped the structure that forms Gringsing industrial sustainability, such as income, social capital, incentives, natural capital, custom law, and traditional institutions. Further analysis was carried out using the Bayesian Network technique and GeNIe tools. In forming the structure of thinking about the sustainability of the Gringsing Weaving industry, the related factors include culture, traditional institutions, natural capital, social capital, to economic factors, such as income and special incentives from the government. Specifically, the role of traditional institutions and government (through special incentives) was analyzed, and it was found that both factors can increase the probability of the sustainability of the gringsing weaving industry. The results of the sensitivity analysis also show that the sustainability of the Gringsing weaving industry is highly influenced (sensitive) to the increasing role of traditional institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".