Mapping and Achievement of Sustainable Development Goals in Indonesia's High-Tech Industries: A Tale of Two Industries
Bibliographic record
Abstract
The study shows an analysis of the non-financial reporting of the United Nations Sustainable Development Goals (SDGs) by a case of companies in the high-tech industry, especially the chemical and pharmaceutical industries.Using a qualitative approach to content analysis, this study focuses on specific issues related to SDGs and reported by high-tech companies.We choose four companies as our case from two different sectors in Indonesia's High-Tech Industries.The findings indicate that SDGs related to this industry are SDGs 3, 4, 5, 7, 8, 9, 12, and 13 for the chemical industry.Meanwhile, for the pharmaceutical industry, the SDGs, which are the main priority, are 3.The pharmaceutical industry generally supports other SDGs but does not contribute to SDGs 11 and 14.SDGs reporting is not exactly the same between these two industries even though they are included in the high-tech industry category.Indicators that have been identified from the results of materiality analysis using an ESG approach and stakeholders' needs, including energy, water, emissions, environmental management, circular economy, climate resilience, community engagement, health and safety, labor and human rights, human capital development, sustainable supply chain, corporate governance, and others.Implications for public policymakers, managers, and other stakeholders are also explored.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".