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Record W4319836873 · doi:10.1002/epa2.1167

Follow the science: The European public health community confronts the first wave of the COVID‐19 pandemic

2023· article· en· W4319836873 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

VenueEuropean Policy Analysis · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)DanishGovernment (linguistics)Public healthPolitical science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Economic growthStakeholderPublic administrationPublic relationsInfectious disease (medical specialty)DiseaseVirologyMedicineEconomics

Abstract

fetched live from OpenAlex

Abstract This paper argues that “following the science” is not always the best strategy. It does so by examining the first phase of the coronavirus disease 2019 (COVID‐19) pandemic in three countries: Denmark, the Netherlands, and Sweden. All three countries possessed highly respected infectious disease agencies with wide stakeholder involvement. Despite this, Danish, Dutch, and Swedish public health agencies underplayed the threat of the COVID‐19 virus, discouraged intrusive mitigation measures, and were slow to admit their mistakes. Countries that trusted their national agencies, specifically the Netherlands and Sweden, witnessed higher mortality. By contrast, the Danish government marginalized its epidemiologists and suppressed the spread of the virus. The paper thus demonstrates the limits of trusting national scientific expertise, even when properly embedded within social networks, during periods of heightened uncertainty.

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)0.041
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.011
Science and technology studies0.0070.002
Scholarly communication0.0000.000
Open science0.0040.003
Research integrity0.0000.001
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 head0.208
GPT teacher head0.358
Teacher spread0.151 · 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