Chronicle of a Death Foretold? The Cultural Exception for Audio-Visual Services in EU Trade Negotiations
Bibliographic record
Abstract
In 2013, the European Union (EU) initiated negotiations for regional trade agreements (RTAs) with the United States (US) and Japan, key trading partners and two of the largest economies in the world. Both countries have strong offensive interests in audio-visual services, a sector that is a sensitive defensive interest to the EU. In this article, it is argued that, besides the likelihood of it being unacceptable to the US and Japan, the 'cultural exception' for audio-visual services as applied in EU trade policy is ill-fitting for its purposes. First, it is too narrow at a cross-sectoral level and, second, it is too wide at the sectoral level. Therefore, it is claimed that the EU should reassess its negotiation strategy vis-à-vis audio-visual services. For inspiration in doing so, this article analyses four cases in which the US and Japan have negotiated bilaterally on audio-visual services with counterparties with defensive interests somewhat similar to the EU's, i.e., Canada, Korea, Switzerland, and India. It concludes that whilst the Canadian approach, i.e., exclusion, is unlikely to be acceptable to the EU's negotiating partners, the Korean, Swiss and, to a lesser extent Indian, approaches provide ample guidance for the EU to rethink its negotiation strategy for audio-visual services.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".