Four Free Trade Agreements GAO Reviewed Have Resulted in Commercial Benefits, but Challenges on Labor and Environment Remain
Bibliographic record
Abstract
[Excerpt] Since 2001, Congress has approved free trade agreements (FTA) with 14 countries. Most were negotiated under Trade Promotion Authority (TPA), which aims to lower trade barriers while strengthening the capacity of trading partners to promote respect for workers? rights and to protect the environment. The Office of the United States Trade Representative (USTR) is responsible for overseeing implementation of the FTAs, and the Departments of Labor (Labor) and State (State) have responsibilities for implementing and managing FTA cooperation projects. GAO was asked to assess progress through FTAs in (1) advancing U.S. economic and commercial interests, (2) strengthening labor laws and enforcement in partner nations, and (3) strengthening partners? capacity to improve and enforce their environmental laws. GAO focused on Jordan, Chile, Singapore, and Morocco, chosen because of their economic, social, and geographic diversity and relatively older FTAs. GAO analyzed relevant trade laws and trends, met with U.S. agencies and foreign government officials, conducted fieldwork in the four countries, and solicited input from the private sector. GAO recommends that agencies update plans for implementing and overseeing FTAs to make the FTAs more effective in producing results. Agencies intend to do so but saw important progress.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".