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Record W4213287388 · doi:10.1021/acs.est.1c06120

Enhancing Scientific Support for the Stockholm Convention’s Implementation: An Analysis of Policy Needs for Scientific Evidence

2022· article· en· W4213287388 on OpenAlexafffund
Zhanyun Wang, Sam Adu-Kumi, Miriam L. Diamond, Ramón Guardans, Tom Harner, Agustín Harte, Natsuko Kajiwara, Jana Klánová, Jianguo Liu, Estefânia Gastaldello Moreira, Derek C. G. Muir, Noriyuki Suzuki, Victorine Pinas, Timo Seppälä, Roland Weber, Bo Yuan

Bibliographic record

VenueEnvironmental Science & Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Toronto
FundersRECETOX Přírodovědecké Fakulty Masarykovy UniverzityBundesamt für UmweltMiljødirektoratetEnvironment and Climate Change Canada
KeywordsConventionPolitical scienceGovernment (linguistics)Work (physics)Civil societyVariety (cybernetics)Action (physics)Public administrationPublic relationsEngineering ethicsBusinessLawEngineeringComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0040.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.017
GPT teacher head0.288
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations80
Published2022
Admission routes2
Has abstractyes

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