The North American Free Trade Agreement (NAFTA): Potential Changes, Effects, and What to Do Concerning the Trucking Industry
Bibliographic record
Abstract
The North American Free Trade Agreement (NAFTA) was established in 1994 to enable various industries to remain competitive in the North American market and to increase trilateral trade among Canada, Mexico, and the United States. Since Donald Trump came into office as President of the United States, there has been potential for reform of NAFTA, and the impact needs to be examined (North American Free Trade Agreement (NAFTA)). The impact of NAFTA on the trucking industry is explored in this study, as the majority of trilateral trade is conducted by trucks crossing borders, which requires freedom of transit. President Trump intends to renegotiate trade agreements, especially with Canada and Mexico. Through these negotiations, the United States seeks to support higher-paying jobs in the United States and to grow the U.S. economy by improving U.S. opportunities to trade with Canada and Mexico (NAFTA). On the other hand, “Mexico has asked the United States to allow its trucks on U.S. roads, and [this] was promised in the first NAFTA agreement but withdrawn by the U.S. Congress, so Mexico is also looking for an anticorruption clause” (Amadeo, 2018). Mexico and Canada do not share the same concerns. Canada is looking for the end of tariffs from the United States on products such as lumber and dairy. Those areas would impact trade and have a trickle effect on the trucking industry, although changes are not likely since there has not been much progress in the negotiation meetings (Amadeo, 2018). It has also been said “that upwards of 60% of NAFTA trade is truck-based … so there is probably little replacement for this trade coming from anywhere since these are the U.S. land borders,” although rail would be the second leading mode (US Trade Experts, 2017). Taking into account all of this information, this research explores which aspects of NAFTA would be affected more than others in a renegotiation. This study uses a strategic audit approach to make recommendations that seek to keep trucking companies involved in trade and with NAFTA.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".