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Record W2625973617 · doi:10.1163/15718069-22021114

Federalism and Liberalization: Evaluating the Impact of American and Canadian Sub-federal Governments on the Negotiation of International Trade Agreements

2017· article· en· W2625973617 on OpenAlexaffabout
Christopher J. Kukucha

Bibliographic record

VenueInternational Negotiation · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsFederalismCooperative federalismInternational tradeNegotiationLegitimacyFree tradeLiberalizationGeneral partnershipPolitical scienceCommercial policyEconomicsPublic administrationPoliticsLaw

Abstract

fetched live from OpenAlex

This article argues that federal systems in Canada and the United States allow for the successful pursuit of sub-federal offensive and defensive priorities in the negotiation of international trade agreements. It is also clear, however, that the coercive American intrastate system limits the relevance of American states in this process, especially when compared to Canada’s relatively cooperative interstate model. Canadian provinces and territories also benefit from ideational considerations, including policy expertise and trust-ties with federal negotiators, which further strengthens sub-federal legitimacy and influence in this policy area. This study evaluates the incremental and significant impact of Canadian and American sub-federal governments across a number of sectors on the negotiations and final legal texts of the Canada-Korea Free Trade Agreement, the Canada-European Union Comprehensive Economic and Trade Agreement, and the Trans-Pacific Partnership.

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)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0110.009
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.366
Teacher spread0.329 · 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 designObservational
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

Citations21
Published2017
Admission routes2
Has abstractyes

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