Bibliographic record
Abstract
Draft. Please don’t cite without permission. Comments encouraged. The European Union is the world’s largest exporter of goods and services and the world’s largest market. It is also a key player in multilateral trade negotiations. It is, however, an unusual ‘trade power ’ in that it is an international organisation, as well as an international actor. This means that its negotiating positions reflect the aggregation of the preferences of the governments of its member states. In the EU trade policy literature this situation is usually captured by the metaphor of the ‘three level game: ’ domestic, European and international. In practice, however, the tendency has been to treat the member states ’ positions as given, effectively collapsing the three-level game into a two-level game with the European level as the ‘domestic ’ level. Moreover, the member states ’ preferences regarding liberalisation or protectionism are taken to be fairly stable. This paper argues that while there is some merit in such assumptions they are becoming increasingly unsustainable. This paper argues that the member states ’ trade policy preferences are shifting and becoming more complex for four reasons, most of which are common to all developed states.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.753 | 0.508 |
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".