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Record W2070686661 · doi:10.1080/10810730.2011.626503

A Flu By Any Other Name: Why the World Health Organization Should Adopt the World Meteorological Association's Storm Naming System as a Model for Naming Emerging Infectious Diseases

2012· article· en· W2070686661 on OpenAlex
Rebecca Schein, Sand Bruls, Vincent Busch, Kumanan Wilson, Larry Hershfield, Jennifer Keelan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health Communication · 2012
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsOttawa HospitalUniversity of Toronto
Fundersnot available
KeywordsCredibilityPublic healthPandemicAssociation (psychology)Context (archaeology)Infectious disease (medical specialty)Public relationsCoronavirus disease 2019 (COVID-19)Political sciencePsychologyHistoryMedicineDiseaseLaw

Abstract

fetched live from OpenAlex

This article explores the factors that contributed to the use of different names for H1N1 by diverse actors in the early stages of the pandemic of 2009 and discusses the implications of inconsistent naming practices for the public's understanding of the virus and the credibility of scientists and health authorities. The authors propose a naming protocol for novel variants modeled after the World Meteorological Association's practice for naming weather events, a model that would enable accurate transmission of technical information among experts and provide a stable name for public use, even in the context of incomplete or changing scientific understanding of the nature of the pathogen.

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.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.345
Teacher spread0.312 · 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