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Record W2139310104 · doi:10.1177/0022002712438351

A Hierarchy of Preferences

2012· article· en· W2139310104 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Conflict Resolution · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsSimon Fraser UniversityUniversity of Toronto
Fundersnot available
KeywordsHierarchyMultitudeArbitrageNetwork formationInternational economicsInternational tradeEconomicsBusinessEconomic geographyPolitical scienceMarket economyFinancial economicsComputer science

Abstract

fetched live from OpenAlex

Bilateral trade agreements have proliferated rapidly within the last two decades, growing into a dense network of multiple ties between countries. The spread of preferential trade agreements (PTAs), however, is not uniform: some countries have signed a multitude of deals, while others remain much less involved. This article presents a longitudinal network analysis method to analyze the patterns of the formation of trade agreements, based on the mutual codetermination of network structure and agreement formation. The findings suggest that PTAs spread endogenously because of structural arbitrage effects in the network, and that they establish a hierarchy among countries. Rich countries form ties with each other and middle-income countries, who themselves create a horizontal layer of PTAs, but least-developed countries are left behind and do not form many ties. Supplanting the multilateral trade regime with preferential agreements therefore creates a system of highly asymmetrical relationships of weaker spokes around a few hubs.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.131
GPT teacher head0.249
Teacher spread0.118 · 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