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Record W3023924701 · doi:10.1177/2043820620924880

Brazil’s war on COVID-19: Crisis, not conflict—Doctors, not generals

2020· article· en· W3023924701 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDialogues in Human Geography · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBureaucracyCoronavirus disease 2019 (COVID-19)PandemicAdministration (probate law)Public healthPolitical science2019-20 coronavirus outbreakPolitical economyPublic administrationSociologyLawMedicineDiseaseVirologyPolitics

Abstract

fetched live from OpenAlex

This commentary first documents the ways in which President Jair Bolsonaro’s administration has evoked securitized discursive strategies that frame Brazil’s national response to COVID-19 as a matter of defense instead of public health. We then ask: What does it mean to talk about the virus and the ways to address it through war-framings? We argue that the Bolsonaro administration has framed the COVID-19 pandemic as an extra-territorial threat in an effort to create internal stability while failing to handle the matter effectively. Such politically motivated spatial framings inhibit an effective response in Brazil and pose a severe threat to public health. Once COVID-19 becomes securitized, the response is framed by the military bureaucracy rather than public health authorities, resulting in dangerous consequences.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.378
Teacher spread0.282 · 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