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Record W333939531

Factoring Cultural Elements into Deciding the ‘Likeness’ of Cultural Products: A Perspective from the New Haven School

2012· article· en· W333939531 on OpenAlexaboutno aff
Shi Jingxia

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
Fundersnot available
KeywordsDiscretionIndoctrinationLaw and economicsIgnorancePolitical scienceJurisprudenceMainstreamFactoringCultural policyCultural diversityPositive economicsSociologyEconomicsLawIdeologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

The ‘likeness’ of imported and domestic products serves as a prerequisite for national treatment under the WTO framework. The article tries to employ the jurisprudence of 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 Article III (national treatment on internal taxation and domestic regulation). After pinpointing the ignorance of cultural values and associated problems, the article focuses on how to factor cultural elements into deciding the ‘likeness’ of cultural products. Specifically, one way suggested is to analyze 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.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.311
Teacher spread0.291 · 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 designQualitative
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
Published2012
Admission routes1
Has abstractyes

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