Factoring Cultural Element into Deciding the ‘Likeness’ of Cultural Products: A Perspective From the New Haven School
Bibliographic record
Abstract
The ‘likeness’ of imported and domestic products serves as a prerequisite for national treatment under the WTO famework. This article tries to employ the jurisprudence of the New Haven School to examine both the GATT/WTO legislative framework on ‘like products’ and relevant judicial practice in ‘Canada-Periodicals’ and ‘China-Publications and Audiovisual Products’ with respect to GATT Art III (national treatment on internal taxation and domestic regulation). After pinpointing the ignorance of cultural values and associated problems, this article focuses on how to factor cultural elements into deciding the ‘likeness’ of cultural products. Specifically, one way suggested is to analyse the role that cultural elements may play in “consumers’ taste and conception”, one of the four traditional criteria in establishing the ‘likeness’. Another is to revisit the ‘aim and effect’ test so that a certain degree of discretion can be left for WTO adjudicators to consider the legitimate cultural policy objectives behind the Members’ regulatory measures. It is the author’s plea that given the widely recognized duality of cultural products, it becomes necessary to reconcile free trade and cultural diversity in the era of economic globalization. Factoring cultural elements into deciding the ‘likeness’ of cultural products acts as a crucial step towards this goal.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".