Trade Wars, COVID-19, USMCA, and Protectionism: Exogenous Factor Influence on U.S- Mexico Supply Chains in the Automotive Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it