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Record W3200836927 · doi:10.1111/disa.12511

The emergence of post‐Westphalian health governance during the Covid‐19 pandemic: the European Health Union

2021· article· en· W3200836927 on OpenAlexaff
Markus Fraundorfer, Neil Winn

Bibliographic record

VenueDisasters · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsWestphalian sovereigntyEuropean unionCorporate governanceInternational Health RegulationsPandemicPreparednessSovereigntyPolitical scienceMulti-level governanceGlobal healthDevelopment economicsCoronavirus disease 2019 (COVID-19)International tradeMedicineBusinessLawEconomicsHealth careInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The response to the Covid-19 pandemic in 2020-21 was dominated by the Westphalian primacy of national territory and sovereignty, significantly worsening and prolonging this crisis. Global platforms for cross-border coordination and cooperation were constrained by national self-interest. Arguably, the lack of a worldwide supranational (or post-Westphalian) authority in health governance is one important structural reason for the fragmented, chaotic, and ineffective response to Covid-19. The failure of Westphalian governance responses to the pandemic provides a unique opportunity for post-Westphalian governance structures to be established and contribute to reforming international pandemic preparedness. While this is unlikely to happen soon at the global level, a comprehensive framework is emerging at the European Union level in the form of a European Health Union. Through a combined conceptualisation of supranational governance and the securitisation process of international health crises, Covid-19 has opened the door to post-Westphalian health governance coordinated by the European Commission.

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)0.013
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.014
Scholarly communication0.0100.005
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.349
Teacher spread0.306 · 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 designNot applicable
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

Citations16
Published2021
Admission routes1
Has abstractyes

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