Recommendation: Complexity in managing plastics — R1/PR7
Bibliographic record
Abstract
This letter discusses the complex nature of plastics, why regulating plastics is a ‘wicked problem’, and the implications of a life cycle approach. The draft Global Plastics Treaty attempts to address two key problems: the cap on production and the problem of chemical additives in plastics. As a ‘wicked’ problem with many conflicting interests, dealing with plastics requires a holistic life cycle approach completely different from the Montreal Protocol. Strict and enforced limits on polymer production would reduce plastics pollution and also encourage a reduction in the range of additives, as limiting production would make mechanical or chemical recycling more viable. Used plastics need to be turned into a commodity rather than a waste, and reducing and standardising the number of different chemical formulations would help by reducing the number of chemicals to be regulated. To achieve these objectives, this letter argues for a regulatory approach based on a forensic analysis that applies extended environmental systems analysis to all the life cycle stages of the plastics value chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.026 | 0.008 |
| Insufficient payload (model declined to judge) | 0.275 | 0.286 |
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".