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Record W2564841248 · doi:10.22230/cjc.2016v41n4a3098

From Outbreak to Pandemic Narrative: Reading Newspaper Coverage of the 2014 Ebola Epidemic

2016· article· en· W2564841248 on OpenAlexaffvenue
Neil Gerlach

Bibliographic record

VenueCanadian Journal of Communication · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsFraming (construction)NarrativeBiosecurityNewspaperPandemicOutbreakMedia studiesPolitical scienceHistoryCoronavirus disease 2019 (COVID-19)SociologyInfectious disease (medical specialty)VirologyMedicine

Abstract

fetched live from OpenAlex

Using framing analysis, this article explores how the English language press organized coverage of the 2014 Ebola epidemic in West Africa. The coverage began with a standard “outbreak” narrative that defined the problem in terms of “primitive” lifeways and inadequate humanitarian aid. However, after the World Health Organization declared an international emergency and after Ebola carriers began to appear in the West, the framing changed toward a “pandemic” narrative that shifted attention away from medical solutions, humanitarian aid, and national safety toward government and military action, biosecurity, and the global species network. This change in the press narrative makes sense to populations in the West because they increasingly live within a “pandemic culture” that has become characteristic of globalized societies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.993

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 studies0.0000.000
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 head0.029
GPT teacher head0.325
Teacher spread0.295 · 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 designObservational
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

Citations30
Published2016
Admission routes2
Has abstractyes

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