National Action Plans: Effectiveness and requirements for the Global Plastics Treaty
Bibliographic record
Abstract
Abstract National Action Plans (NAPs) are a possible implementation measure for the Global Plastics Treaty, through a NAP-based approach. Their effectiveness in other international agreements is contested, and their current format allows for weak, voluntary measures with limited accountability. By analysing stakeholder and country submissions to the Intergovernmental Negotiating Committee (INC) secretariat ahead of INC-2 negotiations in Paris, June 2023, conducting a literature review, and interviewing key actors, this study aims to determine the support that governments and stakeholders have for a NAP-based approach in the Treaty, and identify the key enablers needed to ensure that NAP-based approaches, if adopted in the Treaty, are effective. Results indicate that by INC-2, more than 85% of countries supported a NAP-based approach, suggesting a high chance of this approach being selected as the means of implementation of the Treaty. However, interviewees and literature reviews indicate that NAPs in their current form are not likely to be effective at delivering ambitious Treaty targets. Six key enablers to improve the effectiveness of plastics NAPs are identified. These enablers should be integrated into any plastics NAPs both independently, and as potential requirements of the Treaty to ensure that NAP-based approaches are effective and have the impact intended.
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 | 0.124 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".