Enhancing Scientific Support for the Stockholm Convention’s Implementation: An Analysis of Policy Needs for Scientific Evidence
Bibliographic record
Abstract
The Stockholm Convention is key to addressing the global threats of persistent organic pollutants (POPs) to humanity and the environment. It has been successful in identifying new POPs, but its national implementation remains challenging, particularly by low- and middle-income Parties. Concerted action is needed to assist Parties in implementing the Convention's obligations. This analysis aims to identify and recommend research and scientific support needed for timely implementation of the Convention. We aim this analysis at scientists and experts from a variety of natural and social sciences and from all sectors (academia, civil society, industry, and government institutions), as well as research funding agencies. Further, we provide practical guidance to scientists and experts to promote the visibility and accessibility of their work for the Convention's implementation, followed by recommendations for sustaining scientific support to the Convention. This study is the first of a series on analyzing policy needs for scientific evidence under global governance on chemicals and waste.
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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 | 0.498 | 0.718 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.040 | 0.031 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".