Decision: Consistently inconsistent: The false promise of ‘sustainable’ plastics — R0/PR3
Bibliographic record
Abstract
This perspective explains why the lack of regulation around bioplastics remains a hurdle for the successful development and implementation of a legally binding agreement (the Global Plastics Treaty) by the United Nations Environment Assembly to curb plastic pollution by 2024. For example, bioplastics have been marketed to consumers as the panacea solution to our plastic waste crisis. Of the >400 million tonnes of plastics produced each year, <1% are bioplastics, but the market value of bioplastics is expected to grow. The rapid growth of the environmentally ‘sustainable’ plastic market has resulted in an overwhelming variety of products with different properties and labels, which has led to widespread public confusion, particularly about disposal guidelines. The umbrella term of ‘bioplastics’ describes plastics that can be fully or partially sourced from biological matter, unlike conventional petroleum-based plastics. Within this family of plastics, products can be ‘biodegradable’, ‘oxo-biodegradable’ and ‘compostable’ depending on their chemical composition and the external conditions required at disposal (end-of-life). However, cases of petroleum-based biodegradable plastics have been referred to as bioplastics, which is inaccurate. Overall, this lack of regulation remains a hurdle for the successful development and implementation of the Global Plastics Treaty.
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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 | 0.040 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.072 | 0.040 |
| Insufficient payload (model declined to judge) | 0.022 | 0.020 |
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".