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Record W2306278401 · doi:10.1111/capa.12169

Pre‐existing condition: Taking media coverage into account when preparing for H1N1

2016· article· en· W2306278401 on OpenAlexaff
Kevin Quigley, Colin MacDonald, John M. Quigley

Bibliographic record

VenueCanadian Public Administration · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGlobeMedia coverageGovernment (linguistics)BusinessPublic relationsCoronavirus disease 2019 (COVID-19)Internet privacyPolitical sciencePsychologyMedicineComputer scienceSociologyMedia studies

Abstract

fetched live from OpenAlex

Abstract This article examines the claim that the media exaggerated the threat of H1N1, prompting an immediate demand for limited vaccine. We compare The Australian 's and the Daily Telegraph 's coverage of H1N1 with that of The Globe and Mail , which was comparatively critical of government performance. The events of October 2009 underscore the difficulty and importance of responding to media coverage of “uncertain risks” (Renn ), risks for which there is a lack of scientific basis for decision making and potentially generate anxious public responses.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.346
Teacher spread0.288 · 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
Published2016
Admission routes1
Has abstractyes

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