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Record W3001537798 · doi:10.1007/s10728-019-00389-3

Exploring Models for an International Legal Agreement on the Global Antimicrobial Commons: Lessons from Climate Agreements

2020· article· en· W3001537798 on OpenAlexafffund
Susan Rogers Van Katwyk, Alberto Giubilini, Claas Kirchhelle, Isaac Weldon, Mark Harrison, Angela R. McLean, Julian Savulescu, Steven J. Hoffman

Bibliographic record

VenueHealth Care Analysis · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMcMaster UniversityImpactOttawa Public HealthCentre for Global Health ResearchYork UniversityUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNorges ForskningsrådSocial Sciences and Humanities Research Council of CanadaWellcome Trust
KeywordsCommonsPolitical scienceInternational communityCorporate governancePhilosophy of medicineCollective actionGlobal commonsPoliticsInternational lawHealth informaticsLaw and economicsLawSociologyBusinessMedicineHealth careBiologyEcology

Abstract

fetched live from OpenAlex

An international legal agreement governing the global antimicrobial commons would represent the strongest commitment mechanism for achieving collective action on antimicrobial resistance (AMR). Since AMR has important similarities to climate change-both are common pool resource challenges that require massive, long-term political commitments-the first article in this special issue draws lessons from various climate agreements that could be applicable for developing a grand bargain on AMR. We consider the similarities and differences between the Paris Climate Agreement and current governance structures for AMR, and identify the merits and challenges associated with different international forums for developing a long-term international agreement on AMR. To be effective, fair, and feasible, an enduring legal agreement on AMR will require a combination of universal, differentiated, and individualized requirements, nationally determined contributions that are regularly reviewed and ratcheted up in level of ambition, a regular independent scientific stocktake to support evidence informed policymaking, and a concrete global goal to rally support.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.150
GPT teacher head0.350
Teacher spread0.200 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations52
Published2020
Admission routes2
Has abstractyes

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