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Record W3166600131 · doi:10.1093/publius/pjab010

Explaining Intergovernmental Conflict in the COVID-19 Crisis: The United States, Canada, and Australia

2021· article· en· W3166600131 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePublius The Journal of Federalism · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsConcordia UniversityMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsPresidencyPolitical sciencePresidential systemDemocracyPublic administrationGovernment (linguistics)Political economyPoliticsCoronavirus disease 2019 (COVID-19)Divergence (linguistics)SkepticismIncentiveLawSociologyEconomics

Abstract

fetched live from OpenAlex

Abstract The Covid-19 pandemic produced more significant immediate intergovernmental conflict in the U.S. than in Australia and Canada. This article considers three variables for this cross-national divergence: presidentialism versus parliamentarism; vertical party integration; and strength of intergovernmental arrangements. We find that the U.S. presidential system, contrary to parliamentarism in Canada and Australia, provided an opportunity for a populist outsider skeptical of experts to win the presidency and pursue a personalized style that favored intergovernmental conflict in times of crisis. Then, the intergovernmental conflict-inducing effect of the Trump presidency during the pandemic was compounded by the vertical integration of political parties, which provided incentives for the President to criticize Democratic governors and vice-versa. Third, the virtual absence of any structure for intergovernmental relations in the United States meant that, unlike Australian states and Canadian provinces, American states struggled to get the federal government’s attention and publicly deplored its lack of leadership.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.331
Teacher spread0.268 · 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