ANALISIS UNITED STATE-MEXICO-CANADA AGREEMENT (USMCA) SEBAGAI STRATEGI AMERIKA SERIKAT UNTUK MEWUJUDKAN SURPLUS NERACA PERDAGANGAN PADA MASA PEMERINTAHAN PRESIDEN DONALD TRUMP
Bibliographic record
Abstract
This research aims to explain the strategies employed by the United States to reduce the trade deficit through the USMCA during President Donald Trump's administration. After taking office, President Donald Trump assessed that NAFTA was ineffective and detrimental to the economy. Although trade with Mexico and Canada increased, the U.S. trade deficit also grew. Trump sought to reduce this imbalance and thereby strengthen the domestic economy through fairer and more beneficial trade agreements. To analyze the United States' strategy in reducing the trade deficit, the author uses the concept of Strategic Trade Policy, where the USMCA serves as an intervention by the U.S. government to enhance the competitiveness of domestic industries. Thus, the USMCA is used as a tool to achieve a competitive advantage for the United States. The research method employed by the author in this thesis is a qualitative research method. The findings of this research indicate that President Donald Trump had several strategies to achieve this goal, such as renegotiating NAFTA into the USMCA, protecting workers, increasing employment, boosting growth in the agricultural sector, encouraging new growth in the manufacturing sector, and modernizing regional trade agreements.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".