TPP, CETA and TTIP Between Innovation and Consolidation—Resolving Investor–State Disputes under Mega-regionals
Bibliographic record
Abstract
The United States concluded in 2015, the Trans-Pacific Partnership (TPP) agreement with 11 other countries and the European Union (EU) concluded a revised version of the Comprehensive Economic Trade Agreement (CETA) with Canada in 2016. The provisions on investor – state dispute settlement (ISDS) of the two agreements could not be more different. While the TPP sticks to the traditional system of investor – state arbitration, CETA now contains a two-layered court system with pre-elected tribunal members. The present contribution seeks to analyse the convergences and differences between the two first concluded mega-regionals in greater detail with a special focus on the CETA court system. It asks, to what extent CETA and TPP address current criticisms on ISDS? What do their approaches mean for the international investment governance? And more specifically for the ongoing negotiations on the Transatlantic Trade and Investment Partnership agreement (TTIP) between the EU and the United 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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".