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Non-centralized Coordination during a Transboundary Crisis

2023· book-chapter· en· W4386139525 on OpenAlexaboutno aff
Natalie Glynn

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicImmigration Law and Human Rights
Canadian institutionsnot available
FundersEuropean CommissionUniversity of Cambridge
KeywordsIntergovernmentalismNegotiationPolitical scienceCommissionCollective actionMember statesEuropean unionPublic administrationAction (physics)European commissionBusinessInternational tradeLaw

Abstract

fetched live from OpenAlex

Abstract The coronavirus pandemic of 2020 presented the world with a major issue requiring collective action to appropriately address. Contrasting non-centralization with administrative coordination and centralization as approaches to collective action in federal systems, this chapter examines the decision-making and coordination of freedom of movement policies during the first nine months of the pandemic to understand what coordination arrangements arose in four different federal systems (Australia, Canada, the European Union [EU], and the United States). The comparison highlights the continued importance of regional governments to policymaking in federal systems, the value of the European Commission as both a coordinator and negotiator for policymaking, and the potential that Australia has as a comparator for the EU. It concludes that the development of New Intergovernmentalism has not simply undermined the role of supranational institutions in the EU, but rather the intergovernmental institutions working with the supranational ones have created new dynamics that may be disempowering in one way and empowering in another way for supranational institutions.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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