Overcoming ‘Frankenfoods’ and ‘secret courts’: theresilience of EU trade policy. College of Europe Policy Brief #9.18
Bibliographic record
Abstract
Despite vocal contestation and fears of domestic institutional deadlock over its trade negotiations, the European Union has proven resilient in its trade policy, notably by concluding bilateral trade and investment agreements with important partners, including two across the North Atlantic, Canada and Mexico. > A professionally orchestrated NGO campaign against TTIP and CETA that fed scepticism in several EU member states was crowned with mixed success. Whereas TTIP negotiations were put on hold, CETA finally proceeded. The lack of a broad pan-European opposition and the strong consensual decision-making processes in the EU incentivised policy-makers to accommodate objections, tread carefully and craft compromise. > This process has been further facilitated by the May 2017 Singapore ruling of the Court of Justice of the EU which created room for trade agreements to be split according to exclusive and shared competences. As a result, new agreements such as those with Singapore or Japan now typically embrace three agreements in order to expedite ratification: trade, investment protection and political cooperation. > Separating trade and investment agreements makes it more difficult for special interests to hold free trade agreements hostage and locates parliamentary scrutiny at the European level, while investment agreements face additional ratification by member state parliaments – and a pending Court Opinion.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.034 | 0.015 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".