The Trans-Pacific Partnership: The Challenges of Unraveling the Noodle Bowl
Bibliographic record
Abstract
Abstract Much has been made of the “spaghetti or noodle bowl” problem of overlapping preferential trade agreements (PTA). A new PTA, the Trans-Pacific Partnership (TPP), currently under negotiation between eleven states – Australia, Brunei, Canada, Chile, Malaysia, Mexico, New Zealand, Peru, Singapore, the United States and Vietnam – is intended to help address this issue. The TPP will lower or eliminate barriers to trade among the partners. But officials are not operating in a vacuum as they negotiate this new agreement. Instead, they must contend with rules created in previous agreements, many of which link TPP partners together in ways that constrain their options now. This article looks in detail at negotiations over market access in goods to better understand the tradeoffs faced by negotiators. Unfortunately, some of the decisions made so far after three years of talks suggest that the TPP market access deal could end up being just another twisted noodle in a crowded bowl.
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 | 0.032 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 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".