Understanding the Trans-Pacific Partnership
Bibliographic record
Abstract
The Trans-Pacific Partnership (TPP) is a big deal in the making. With the Doha Round of multilateral trade negotiations at an impasse, the TPP negotiations have taken center stage as the most significant trade initiative of the 21st century. As of December 2012, negotiators have made extensive progress in 15 negotiating rounds since the talks began in March 2010, though hard work remains to finish the deal in the coming year or so. Despite this effort, however, the TPP is not well understood. In part, the reason lies in the dynamism of the TPP initiative. Unlike other free trade pacts, the growing membership as the talks have proceeded and the broad range, complexity, and novelty of the issues on the agenda have made it difficult to track the substantive detail and progress of the talks. This Policy Analysis aims to remedy this problem by providing a reader's guide to the TPP initiative. It first assesses how much the TPP countries are alike and like-minded in their pursuit of a comprehensive trade deal. It then examines the current status of the talks, the major substantive sticking points, and the implications of Canada and Mexico joining the talks as well as prospective membership of other countries. The Policy Analysis then looks ahead to how the TPP could advance economic integration in the Asia-Pacific region and the implications for trade relations with China.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".