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Record W2342508285 · doi:10.1017/s0008423916000184

South American Market Integration: The Argentina-Brazil Rivalry Myth and Motivations for the Southern Common Market

2016· article· en· W2342508285 on OpenAlexaff
Trygve Alexander Giaever, Julian Schofield

Bibliographic record

VenueCanadian Journal of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsConcordia University
Fundersnot available
KeywordsRivalryRegional integrationLeverage (statistics)Political economySingle marketEconomic integrationTreatyPolitical scienceInternational tradeMarket shareEconomicsEconomyEuropean unionLawFinance

Abstract

fetched live from OpenAlex

Abstract This paper revisits and rebuts the mainstream view that Brazil and Argentina were led to form the Southern Common Market to end more than a century of rivalry and competition. We find the elements characterizing an interstate rivalry diminishing in the nineteenth century through the promotion of peaceful settlements and strategic alliances while those that could prompt security concerns disappeared years before the Southern Common Market was formed. Except for diplomatic disputes over the distribution of shared water resources, a disagreement settled in 1979, the decades preceding the Treaty of Asuncion were typified by security alliances, co-operation on economic complementarity and the promotion of bilateral institutions. We find little evidence for the implied security motivations being proposed in the literature. Rather, the establishment of the Southern Common Market was driven primarily by Argentina's and Brazil's desire to improve economic performance and advance political leverage through the promotion of a common stance in global affairs. This view challenges a common component in integration theory that, as applied to the European Union and elsewhere, asserts the privileged role of security concerns as prime driver for integration. This matters because there is a misapprehension that affects both the theory about integration as well as the formulation of policy prescriptions for South America.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.011
Scholarly communication0.0060.003
Open science0.0000.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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