Dissents in Regulating Cultural Trade and Its Mechanisms of Dispute Settlements in Multilateral Forum: Analyzing the Roles of UNESCO and WTO
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".