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Record W2955470652 · doi:10.22230/cjc.2019v44n2a3341

Visualizing Ebola: Hazmat Suit Imagery, the Press, and the Production of Biosecurity

2019· article· en· W2955470652 on OpenAlexaffvenue
Neil Gerlach

Bibliographic record

VenueCanadian Journal of Communication · 2019
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiosecurityNarrativeGuard (computer science)HistoryGeographyArtEcologyComputer science

Abstract

fetched live from OpenAlex

Background Examining press coverage of the Ebola outbreak in West Africa in 2014, this article analyzes the work of the hazmat suit as a visual signifier of disease. Analysis Hazmat imagery from Africa operated to make the disease visible, both “othering” it and contributing to fantasies of containment. In American imagery, the suit became a figure of biosecurity and reassurance, while also connoting the prospect of American “diseaseability.” Conclusion and implications African hazmat imagery reinforced pre-existing schema for understanding Ebola within a news category while American imagery straddled the boundary between the geography of disease fear and the imagined immunological community, potentially destabilizing press narratives of reassurance.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.008
Scholarly communication0.0060.005
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.309
Teacher spread0.284 · 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.

Study designQualitative
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

Citations9
Published2019
Admission routes2
Has abstractyes

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