Megaregulation Contested : Global Economic Ordering After TPP
Bibliographic record
Abstract
The Japan-led Trans-Pacific Partnership (CPTPPA) of 2018 is the most far-reaching 'megaregional' economic agreement in force, with several major countries beyond its eleven negotiating countries also interested. Still bearing the stamp of the original US involvement before the Trump-era reversal, TPP is the first instance of 'megaregulation': a demanding combination of inter-state economic ordering and national regulatory governance on a highly ambitious substantive and trans-regional scale. Its text and ambition have influenced other negotiations ranging from the Japan-EU Agreement (JEEPA) and the US-Mexico-Canada Agreement (USMCA) to the projected Pan-Asian Regional Comprehensive Economic Partnership (RCEP). This book provides an extensive analysis of TPP as a megaregulatory project for channelling and managing new pressures of globalization, and of core critical arguments made against economic megaregulation from standpoints of development, inequality, labour rights, environmental interests, corporate capture, and elite governance. Specialized chapters cover supply chains, digital economy, trade facilitation, intellectual property, currency levels, competition and state-owned enterprises, government procurement, investment, prescriptions for national regulation, and the TPP institutions. Country studies include detailed analyses of TPP-related politics and approaches in Japan, Mexico, Brazil, China, India, Indonesia, and Thailand. Contributors include leading practitioners and scholars in law, economics, and political science. At a time when the WTO and other global-scale institutions are struggling with economic nationalism and geopolitics, and bilateral and regional agreements are pressed by public disagreement and incompatibility with digital and capital and value chain flows, the megaregional ambition of TPP is increasingly important as a precedent requiring the close scrutiny this book presents.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".