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Record W4238311343 · doi:10.3138/cras.42.1.105

Rehearsing for the Plague: Citizens, Security, and Simulation

2012· article· en· W4238311343 on OpenAlexvenueno aff
Melanie Armstrong

Bibliographic record

VenueCanadian Review of American Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsGovernment (linguistics)RehearsingRestructuringPreparednessPlague (disease)IdeologyHealth carePolitical sciencePopulationEthnographyPoliticsEmergency managementPublic relationsPublic administrationSociologyMedicineEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Abstract: Daily practices of bioterrorism preparedness are producing a security community in which citizens are bound together by common biological risk, access to care during times of crisis, and the ability and authority to provide care in an emergency. Through the study of national-level exercise programmes and city-wide preparedness plans in Albuquerque, New Mexico, this ethnographic research asks how communities are materially and ideologically organized around the idea of mitigating biological risk. The dual acts of planning for bioterrorism and simulating a response prescribe a distinct role for government in caring for a population, and not just during times of crisis. This paper explores the outcomes of publicly rehearsing the care practices of government through bioterror simulation by considering how restructuring health systems around the idea of biopreparedness confounds the specter of war with life-giving acts of health care, offering citizens a way of living within the state of emergency.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.414
Teacher spread0.340 · 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 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

Citations33
Published2012
Admission routes1
Has abstractyes

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