Preliminary Development of Indicators for Assessing the Sustainability of Indonesia’s Natural-Dye-Based Batik Industry
Bibliographic record
Abstract
Indonesia’s batik industry is growing rapidly, including the segment specializing in natural dyes. However, this has produced concerns regarding sustainability deriving from, for example, the use of environmental pollutants in the field. This research aims to propose a set of preliminary indicators to facilitate the assessment of the sustainability of the natural-dye-based batik industry. We selected Batik Preketek, a company in Pekalongan City, Indonesia, as a case study and received support from representative panellists who were knowledgeable on these issues. We employed a mixed-methods approach, with data collected from field observations, laboratory analyses, structured and semi-structured interviews and secondary sources. The validated indicators were then applied to assess the current sustainability of batik production. The indicators used included five indicators from the environmental dimension, four from the economic dimension and six from the social dimension. Assessment of the company’s sustainability level produced a score of 77.50, indicating that it could be categorized as sustainable. The instrument developed was proved capable of capturing major sustainability issues and delivering prioritized strategies to improve sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".