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Record W4383908534 · doi:10.17645/pag.v11i3.6800

Multilevel Trade Policy in the Joint‐Decision Trap? The Case of CETA

2023· article· en· W4383908534 on OpenAlexafffundabout
Jörg Broschek

Bibliographic record

VenuePolitics and Governance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsJoint (building)Trap (plumbing)Political scienceMode (computer interface)PoliticsComparative casePerspective (graphical)Joint ImplementationInternational relationsField (mathematics)Public economicsEconomicsInternational tradeBusinessEmissions tradingEngineeringComputer scienceLaw

Abstract

fetched live from OpenAlex

Wallonia’s refusal to ratify CETA in October 2016 suggests that multilevel trade politics may increasingly be subject to the pitfalls of joint decision-making, or even a joint-decision trap. This article, however, presents a more nuanced perspective that builds on a comparative analysis of intergovernmental configurations that underpinned constituent units’ participation in CETA in the four formal federations Canada, Belgium, Germany, and Austria. It shows, firstly, that joint decision-making is only one mode of intergovernmental trade policy coordination that needs to be distinguished from others. Second, joint decision-making rarely leads to a joint decision trap as actors seek to bypass the institutional constraints entailed in this mode of intergovernmental coordination. The study has implications beyond the field of trade policy as it contributes to the comparative analysis of intergovernmental relations in Canada and Europe.

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)0.017
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: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0100.010
Scholarly communication0.0130.006
Open science0.0010.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.000

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 head0.050
GPT teacher head0.340
Teacher spread0.291 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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