Trump Administration Continues Trade Negotiations with Major Trade Partners
Bibliographic record
Abstract
In the fall of 2019, the Trump administration reached several trade arrangements, some of them tentative, with important U.S. trade partners. On October 11, 2019, China and the United States announced a preliminary trade deal subject to finalization—one that came after more than a year of escalating tariffs. Just a week earlier, the United States had signed two trade agreements with Japan, one regarding tariff reductions and the other regarding digital trade. None of these deals appear to require subsequent congressional approval in the eyes of the executive branch, unlike the earlier United States-Mexico-Canada-Agreement (USMCA), which was signed in November 2018 and whose fate in Congress appears promising as of mid-December of 2019. In addition to these trade arrangements, the fall of 2019 saw several developments in trade relations between the United States and the European Union tied to the long-running trade disputes.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.083 | 0.046 |
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".