The politics of anti-plastics activism in Indonesia and Malaysia
Bibliographic record
Abstract
Abstract Research on anti-plastics activism in Indonesia and Malaysia, although increasing somewhat in recent years, is sparse and patchy. Interviews with local activists and a review of the existing literature, however, does suggest this activism is intensifying. Activists are educating people of the health and ecological risks of plastics, and operating nonprofit organizations to recycle and repurpose plastics. They are organizing cleanups and advocating for marginalized waste workers. And they are lobbying governments for stricter regulations, exposing illegal operations, and building transnational advocacy networks. Collectively, these strands of activism appear to have the potential to aggregate eco-actions and decrease plastic pollution. In the coming years, however, given the power of the global plastics industry and the nature of politics within Indonesia and Malaysia, pro-plastics corporations and industry allies are likely going to increasingly contest anti-plastics narratives and strive to undermine efforts to address the root causes of plastic pollution, including rising sales of single-use plastics by transnational corporations, the dumping and burning of unrecyclable plastics from high-income countries, and inadequate waste infrastructure and regulatory enforcement. Further research on how this politics is affecting the power and effectiveness of anti-plastics activism, the article concludes, is going to be essential for improving plastics governance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".