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Record W2267105828

Building the Americas: Governing Market-Led Regional Integration

2007· article· en· W2267105828 on OpenAlexaff
Michèle Rioux

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHegemonyCorporate governancePolitical scienceRegionalism (politics)Political economyDiversity (politics)Single marketEconomic systemEconomic geographyDevelopment economicsInternational tradeGeographySociologyEconomicsPoliticsLawManagement
DOInot available

Abstract

fetched live from OpenAlex

Bringing together, in a common and single space, a continent characterized by such diversity and extraordinary asymmetries, is not an easy task. The are still composed of different spaces, countries, cultures and societies not yet sharing a common sense of destiny. But if there is no single America, a new regionalism has emerged and represents a roadmap to a new governance framework implying the deepening of liberal economic and institutional reforms. Resistance and obstacles remain numerous and perhaps the biggest challenge is to address the complex issues related to North-South integration within the hemisphere and the hegemonic position of the United States. If the process of Building the Americas can be depicted as an attempt to define governance in an era of market-led integration, the paradox is that its eventual success depends on its capacity to transcend the immediate commercial and trade orientations and create a true Community of Democracies. In other words, Building the Americas must now go beyond market-led integration, beyond US hegemonic governance, and, foremost, recognize the diversity of cultures, values and identities in the hemisphere.

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)0.010
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.016
Scholarly communication0.0100.007
Open science0.0010.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.298
Teacher spread0.289 · 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 designNot applicable
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
Published2007
Admission routes1
Has abstractyes

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