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Record W2527819544 · doi:10.1017/s1474745616000446

Reforming WTO-Civil Society Engagement

2017· article· en· W2527819544 on OpenAlexafffund
Erin Hannah, James Scott, Rorden Wilkinson

Bibliographic record

VenueWorld Trade Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsThe King's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCivil societyNegotiationPoliticsContext (archaeology)Political scienceValue (mathematics)Political economyPublic administrationSociologyLaw

Abstract

fetched live from OpenAlex

Abstract Civil society organizations are often seen as playing a crucial role in helping to mitigate the exclusion of weaker states, giving voice to marginalized communities, and raising environmental and developmental concerns within the trade system. The politicization and demystification of the global trade agenda by civil society also opens up space for a more diverse set of actors to influence trade negotiations. This article examines the evolution of the WTO secretariat's engagement with civil society within this context and argues that the dominant mode of engagement, as manifest in WTO Public Forums and civil society participation in ministerial conferences, is no longer fit for purpose. Rather it reflects an outmoded strategy that once served to underscore the existence and value of the WTO as an international organization and works to neutralize political contestation and publicly promote the benefits of free trade. It is now in need of reform.

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)0.047
metaresearch head score (Gemma)0.035
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 score0.047
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.023
Scholarly communication0.0170.011
Open science0.0020.010
Research integrity0.0140.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.067
GPT teacher head0.371
Teacher spread0.304 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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