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Record W4387505478 · doi:10.5354/0719-9368.2023.70137

Treatment of Cultural Services in Central American Countries’ Preferential Trade Agreements

2023· article· en· W4387505478 on OpenAlexafffund
Gilbert Gagné, Cassandre Nycz

Bibliographic record

VenueLatin American Journal of Trade Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsUniversité de SherbrookeBishop's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsScope (computer science)Goods and servicesInternational tradeFree tradeThe artsNewspaperPolitical scienceCommercial policyCultural policyMarket accessBusinessEconomyEconomicsGeographyLawAgriculture

Abstract

fetched live from OpenAlex

Cultural products (including goods and services) encompass visual, performing and literary arts, as well as newspapers, magazines, books, films, video and music recordings, radio and television, either in traditional or digital format. To the extent that they reflect the cultural identities of states, their treatment in international trade has been debated as to whether, or the extent to which, they should be exempted from trade obligations. The proliferation of preferential trade agreements and of digital platforms has rendered the debate ever more salient. The article summarizes the provisions on cultural services in Central American countries’ preferential trade agreements and discusses the scope of these provisions, in light of the cultural policy measures involved and states’ ability to pursue cultural policies. The countries considered are those belonging to the Central American Common Market, namely Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama. These countries share some characteristics which make them worth considering in regard to the trade and culture debate. They also vary widely with respect to the number and scope of their commitments and/or exceptions relating to culture within the preferential trade agreements to which they are parties. In turn, such significant variations are primarily attributable to the importance each Central American country attaches to the protection of its cultural sector.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.006
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.324
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes2
Has abstractyes

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