MétaCan
Menu
Back to cohort
Record W4319787714 · doi:10.1080/09692290.2022.2154244

The Geneva effect: where officials sit influences where they stand on WTO priorities <sup>*</sup>

2023· article· en· W4319787714 on OpenAlexaff
Bernard Hoekman, Robert Wolfe

Bibliographic record

VenueReview of International Political Economy · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsQueen's University
Fundersnot available
KeywordsBureaucracyAutonomyPrioritizationPoliticsPolitical scienceMember statesPublic administrationCapital (architecture)Political economyEconomicsInternational tradeEuropean unionLaw

Abstract

fetched live from OpenAlex

Do representatives of member states in Geneva and officials based in capitals agree on priorities for cooperation in the World Trade Organization? Exploiting an original survey of trade policy officials, we find that respondents representing their countries in Geneva often accord substantially different priorities to institutional reform and policy issues than officials based in capitals. We hypothesize that this ‘Geneva effect’ reflects bureaucratic capacity in capitals and autonomy of Geneva-based officials, and that the effect should be smaller for officials from OECD member states, given extensive interaction outside the WTO to define good regulatory policies and address trade issues of common concern. Empirical analysis supports these hypotheses but also reveals differences in prioritization between Geneva and capital-based officials from OECD countries for specific issues. The results suggest that the prospects of international cooperation may be influenced not only by well-understood differences between states that reflect material interests and domestic political economy drivers, but by differences in relative priorities accorded to issues by officials representing states in international organizations and officials based in capitals.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.037
GPT teacher head0.277
Teacher spread0.239 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueReview of International Political EconomySame topicGlobal trade and economicsFrench-language works237,207