Conceptual framework for identifying polymers of concern
Bibliographic record
Abstract
With the increasing global concern over plastics' environmental and human health impacts, the urgency for effective regulatory measures is evident. The UN Environment Assembly's initiative to establish an international, legally binding instrument via the Intergovernmental Negotiating Committee (INC) on Plastic Pollution marks a significant step toward addressing this issue. However, the vast diversity of plastic types and their myriad applications present a complex challenge in pinpointing the most critical targets for regulation. This study builds on the existing body of literature to outline potential key criteria for identifying Polymers of Concern (PoC). We recommend a dual-focused definition of PoCs considering both (1) the type of the plastics and (2) their domain of applications based on the environmental and human health impacts throughout the polymer's life cycle. Recognizing the current gaps in our understanding of the full spectrum of plastics' impacts across their life cycles, we suggest adopting a precautionary approach that factors in the volume of plastics entering natural ecosystems alongside their life cycle impacts as reported in the literature. We then bring forward existing data on the assessment of some of the main polymer types and applications. We propose that policymakers examine a wide spectrum of strategies including not only bans and phaseouts but also economic incentives, innovation, and the redesign of plastic materials and products to mitigate the adverse impacts of PoCs. We further emphasize the importance of thoroughly assessing the feasibility, costs, and environmental, social and economic implications of alternative materials to avoid “regrettable substitution.” We conclude by identifying existing knowledge gaps and emphasizing the need for further research to refine the proposed criteria for identifying PoCs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".