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Record W2941731957 · doi:10.20473/jgs.13.1.2019.51-62

Dissents in Regulating Cultural Trade and Its Mechanisms of Dispute Settlements in Multilateral Forum: Analyzing the Roles of UNESCO and WTO

2019· article· en· W2941731957 on OpenAlexaboutno aff
Annisa Pratamasari

Bibliographic record

VenueJurnal Global & Strategis · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
Fundersnot available
KeywordsInternational tradeContradictionConventionGoods and servicesService (business)International trade lawMeaning (existential)Trade in servicesPolitical scienceFree tradeBusinessEconomicsLawEconomy

Abstract

fetched live from OpenAlex

Even though the volume of trade in goods and service increases in some advanced economy countries, trade in cultural goods and services remain in neglected terrain within multilateral frameworks. WTO has indeed highlighted the importance of cultural goods’ protection; however, its meaning and scopes, terms of protection, and dispute settlement on cultural goods and services have remained vague. On the other hand, UNESCO shed some lights on the trade of cultural goods and services, but some of the articles in its convention arises some contradiction with WTO clauses. The cases presented in this paper are all related to the culture trade, namely Canada for a periodical case (DS31), Turkey’s taxation of foreign film (DS43), Canada’s case on film distribution service (DS117), and China’s case on publications and audiovisual products (DS363). This paper aims to analyze how those cases were handled in the dispute settlement of WTO and supposed to be handled by both UNESCO and WTO. I would like to draw some lessons from the inadequate dispute settlement in WTO on culture trade, then proceed to formulate some suggestions of how cultural trade-related disputes are supposed to be formulated.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.297
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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