The UK’s new free trade agreements in the Asia-Pacific: how closely is it adopting US trade regulation?
Bibliographic record
Abstract
In a global economy and system increasingly defined by new developments and complexities in trade, whose rules and regulations govern that trade matter. The UK has embarked on a new post-Brexit trade policy, signing its first wholly new free trade agreements (FTAs) with Australia and New Zealand. It is also in negotiations to join the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP) as part of the UK’s aspirations to become an integral part of the Asia-Pacific trading community. This study’s research and text analysis on the UK’s bilateral FTAs with Australia and New Zealand reveals high levels of similarity with two larger regional agreements heavily imprinted with US trade regulatory norms—this being the CPTPP itself and the United States–Mexico–Canada Agreement (USMCA). The UK’s revealed willingness to strongly align itself with US trade regulatory norms has important implications for the Asia-Pacific. It also raises some key issues on what kind of trade partner the region might expect a post-Brexit ‘Global Britain’ to become, and how the UK’s deeper planned engagement with the Asia-Pacific could affect its strategic dynamics. This could significantly depend on how closely the UK is pulled over time into the US’ trade regulatory orbit.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".