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Record W4229040510 · doi:10.1111/1758-5899.13092

Emerging Powers, Leadership, and <scp>South–South</scp> Solidarity: The Battle Over Special and Differential Treatment at the <scp>WTO</scp>

2022· article· en· W4229040510 on OpenAlexafffund
Kristen Hopewell

Bibliographic record

VenueGlobal Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsUniversity of British Columbia
FundersCanada Research Chairs
KeywordsEmerging marketsSolidarityBattleNegotiationChinaDeveloping countryInternational tradeWorld tradeBusinessPolitical scienceEconomicsEconomic growthLawPoliticsFinance

Abstract

fetched live from OpenAlex

Abstract Emerging powers, such as China and India, have used claims of developing world leadership and South–South solidarity to strengthen their bargaining position in WTO negotiations. Yet analysis of the growing battle over special and differential treatment (SDT) suggests that such claims are increasingly tenuous. The question of how emerging economic powers should be classified and treated under global trade rules has become an acute source of conflict in the trade regime. The emerging powers insist on access to SDT as an unconditional right of developing countries. But in a debate dominated by the emerging and established powers, the interests of most developing countries have been largely overlooked. Drawing on the cases of agriculture and fisheries – two areas of international trade of particular importance to the developing world – I show that extending SDT to the emerging powers is increasingly problematic for global development. In these areas, many emerging economies are now among the world's largest subsidizers, and the harmful effects of their policies are felt most keenly by other developing countries. Granting SDT to exempt emerging subsidizers from WTO disciplines would therefore undermine efforts to use global trade rules to promote global development, as well as to protect the environment.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.279
Teacher spread0.256 · 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 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

Citations13
Published2022
Admission routes2
Has abstractyes

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