Bibliographic record
Abstract
The chapter offers an insight into the highly fluid field of digital trade rulemaking as a direct reaction to digital transformations. Against the backdrop of the evolution of the trade policy discourse beyond online trade in goods and services and the emergence of new digital protectionism, the chapter explores the dynamics of digital trade regulation in the past decade in a complex geopolitical setting by looking at some broader trends, as well as distinct regulatory models (exemplified by the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the United States–Mexico–Canada Agreement (USMCA), the European Union–United Kingdom Trade and Cooperation Agreement (TCA), and the Regional Comprehensive Economic Partnership (RCEP)) and the new templates of the digital economy agreements (DEAs) that also signal room for regulatory innovation in trade law. The chapter’s enquiry seeks to identify new design elements found in preferential trade agreements (PTAs), to trace how these have diffused over time, and to ask what their implications for domestic regulatory space may be. Finally and linking to the multilateral discussions on electronic commerce, the chapter tests to what extent PTAs have worked as regulatory laboratories and whether some of the newly emerged digital trade rules can be multilateralised.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.057 |
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".