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Record W2001097546 · doi:10.2105/ajph.2012.300788

Dupras and Williams-Jones Respond

2012· article· en· W2001097546 on OpenAlexaff
Charles Dupras, Bryn Williams–Jones

Bibliographic record

VenueAmerican Journal of Public Health · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsConcordia University
Fundersnot available
KeywordsDeclarationPandemicWritTransparency (behavior)Public healthPerceptionRisk perceptionPsychologyPolitical sciencePublic relationsSocial psychologySociologyMedicineCoronavirus disease 2019 (COVID-19)LawInfectious disease (medical specialty)NursingDisease

Abstract

fetched live from OpenAlex

In his letter, Card argues that “it is not the word ‘pandemic’ that needs improvement, but rather our plans and our communication about what the word means—and doesn’t.” We totally agree with and support this statement. We did not aim to critique the formal definition of the word “pandemic.” Instead, we argued that improving communication should not only mean spreading the true definition of technical medical words, but also the recognition that definitions of risk and assessment of what risks are acceptable vary greatly across peoples and contexts. Thus, we argued for a bidirectional transparency—that is, paying greater attention to public perception—to improve risk communication between the experts and the lay public. Building on Doshi’s article,1 Card suggests that the word “pandemic” has never been (formally) associated with levels of severity, and thus “a pandemic is simply an epidemic writ large.” Although this may be (formally) correct, it is also only half true because it does not account for the kind of risk perception engendered by the World Health Organization (WHO) 2009 declaration of a Phase 6 pandemic, which was aimed at both the lay public and the medical community. To say that “pandemicity and severity are separate constructs” ignores the clear variability in risk perception. It also promotes a utopian view of formal definitions that unreasonably minimizes the impact that changing the description of the characteristics of a pandemic (by the WHO on their official Web site in Spring 2009)2 might have had on the development of the influenza A (H1N1) crisis. Severity was implicit in the use of the word “pandemic” by public health authorities. Ignoring this reality—combined with risk amplification by the media—resulted in a worldwide panic that might have been prevented with a more cautious communication of the facts of the case, including open discussions of the scientific uncertainty associated with H1N1 incidence and severity. Now, if “a pandemic is simply an epidemic writ large,” why does the WHO still include in its description of the characteristics of an influenza pandemic the criteria “against which the human population has no immunity”?1 Is this not in fact an aspect of severity?

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.054
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0090.013
Open science0.0040.006
Research integrity0.0540.047
Insufficient payload (model declined to judge)0.0190.008

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 head0.091
GPT teacher head0.406
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2012
Admission routes1
Has abstractyes

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