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Record W2755052596 · doi:10.1111/laps.12032

From Middle Powers to Entrepreneurial Powers in World Politics: Brazil’s Successes and Failures in International Crises

2017· article· en· W2755052596 on OpenAlexaff
Feliciano de Sá Guimarães, María Herminia Tavarés de Almeida

Bibliographic record

VenueLatin American Politics and Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsAgency (philosophy)Position (finance)Power (physics)PoliticsForeign policyHard powerPolitical scienceEconomic systemPolitical economyEconomicsSociologySocial scienceFinanceLaw

Abstract

fetched live from OpenAlex

Abstract This article uses the concept of entrepreneurial powers to discuss how and under what circumstances Brazil successfully accomplishes its goals in international crises. The concept of entrepreneurial power focuses on systematic evidence of middle-power behavior and its relation to foreign policy tools. Brazil resorts to three agency-based foreign policy tools that are the substance of its entrepreneurial power. These instruments are always mediated by a structural condition, the dominant power pivotal position in the crisis. This study applies qualitative comparative analysis methodology to 32 international crises since the early 1990s in which Brazil played a role. It finds that for regional crises, the use of only one agency-based tool is sufficient for success, regardless of the dominant power position; and for global crises, the use of only one agency-based tool is a necessary and sufficient condition for Brazil to accomplish its goals, despite the dominant power position on the issue.

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)0.006
metaresearch head score (Gemma)0.015
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.010
Scholarly communication0.0050.003
Open science0.0000.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.431
Teacher spread0.374 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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