Bibliographic record
Abstract
There is a worldwide movement toward greater liberalization of international trade.This is seen at a global level through the Doha round of negotiations of the World Trade Organization.Regional examples include the North American Free Trade Agreement (NAFTA)' between the United States, Canada, and Mexico, the agreement between five countries in Central America, the Dominican Republic, and the United States (CAFTA-DR), 2 and trading agreements between countries in other regions such as the Association of Southeast Asian Nations (ASEAN) and the Southern African Customs Union (SACU). 3 The United States has established bilateral agreements with Israel, Jordan, Chile, Singapore, Australia, Morocco, Bahrain and Oman, and continues negotiations or is in the approval process with South Korea, Peru, Panama, Colombia, Thailand, and the United Arab Emirates.The United States also is working toward comprehensive agreements that will create the Free Trade Area of the Americas.4 It is a busy time at the Office of the U.S. Trade Representative and the associated agencies involved in these negotiations.The United States Environmental Protection Agency (USEPA) continues to be an active part of the negotiating team to ensure that environmental issues are appropriately addressed.
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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 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".