Trade Wars, COVID-19, USMCA, and Protectionism: Exogenous Factor Influence on U.S- Mexico Supply Chains in the Automotive Industry
Bibliographic record
Abstract
This research explores what the impacts of COVID-19, the U.S-China trade war, and the implementation of North American Trade Agreement (NAFTA) as the United States, Mexico Canada (USMCA) Trade Agreement, have had on U.S.-Mexico trade relations, focusing on the automotive industry. With rising trends of protectionism in international trade, this research focuses on the language that Tesla and General Motors company sites in Mexico used from 2021 to March 2023 in their released articles to the public and how frequently the variables of COVID19, the U.S China trade war, USMCA, and protectionism were discussed. Articles in both Spanish and English were included in this analysis. It is of particular importance to focus on the automotive industry as it is the largest industry in trade for Mexico with the U.S. In the 2021-2023 period, the Mexico General Motors and Tesla company websites collectively released 97 articles. The sample greatly consisted of articles from General Motors. However, because General Motors is much more established in Mexico than Tesla, this is expected. The presence of these variables of COVID19, USMCA, U.S. China Trade War, and rising protectionism caused major impacts to the global economy. Through content analysis of the released media articles from General Motors and Tesla, I found that these factors - which deeply impacted the global economy – also impact smaller sectors of the economy, namely automotive supply chains.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".