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Record W3166377604 · doi:10.1093/publius/pjab011

Explaining Intergovernmental Coordination during the COVID-19 Pandemic: Responses in Australia, Canada, Germany, and Switzerland

2021· article· en· W3166377604 on OpenAlexaboutno aff
Johanna Schnabel, Yvonne Hegele

Bibliographic record

VenuePublius The Journal of Federalism · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsJurisdictionCoronavirus disease 2019 (COVID-19)Political sciencePublic administrationPandemicProcurementMember statesBusinessEconomicsEconomic policyLawEuropean unionManagement

Abstract

fetched live from OpenAlex

Abstract The COVID-19 pandemic required prompt action from governments all over the world. In federal systems, it can be important or beneficial to coordinate crisis management between the various governments. The extent to which intergovernmental coordination occurred and the form it took (vertical or horizontal) varied across countries and regarding the measures taken. By examining the introduction and the subsequent easing of containment measures and the procurement of medical supplies in Australia, Canada, Germany, and Switzerland, this article identifies the circumstances under which intergovernmental coordination occurs. Surprisingly, the existence of strong intergovernmental councils did not lead to closer intergovernmental coordination. Governments coordinated more intensively when jurisdiction was shared, problem pressure was high, and measures were(re-)distributive in nature. Vertical coordination was more likely when vertical intergovernmental councils existed and powers were shared.

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)0.010
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 score0.060
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0010.003
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 head0.048
GPT teacher head0.326
Teacher spread0.278 · 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

Citations47
Published2021
Admission routes1
Has abstractyes

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