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Record W3014276017 · doi:10.1007/s10728-019-00388-4

Governing the Global Antimicrobial Commons: Introduction to Special Issue

2020· article· en· W3014276017 on OpenAlexafffund
Steven J. Hoffman, Julian Savulescu, Alberto Giubilini, Claas Kirchhelle, Susan Rogers Van Katwyk, Isaac Weldon, Brooke Campus, Mark Harrison, Hannah Maslen, Angela R. McLean

Bibliographic record

VenueHealth Care Analysis · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of OttawaOttawa Public HealthCentre for Global Health ResearchMcMaster UniversityYork University
FundersArts and Humanities Research CouncilCanadian Institutes of Health ResearchNorges ForskningsrådSocial Sciences and Humanities Research Council of CanadaWellcome Trust
KeywordsPhilosophy of medicineCommonsPublic healthAntimicrobial stewardshipAntimicrobialPolitical scienceAntibiotic resistanceMedicineLawBiologyAlternative medicineMicrobiology

Abstract

fetched live from OpenAlex

Antimicrobial resistance is one of the greatest public health crises of our time. The natural biological process that causes microbes to become resistant to antimicrobial drugs presents a complex social challenge requiring more effective and sustainable management of the global antimicrobial commons-the common pool of effective antimicrobials. This special issue of Health Care Analysis explores the potential of two legal approaches-one long-term and one short-term-for managing the antimicrobial commons. The first article explores the lessons for antimicrobial resistance that can be learned from recent climate change agreements, and the second article explores how existing international laws can be adapted to better support global action in the short-term.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.008
GPT teacher head0.267
Teacher spread0.258 · 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.

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

Citations6
Published2020
Admission routes2
Has abstractyes

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