The The Impact of the Renegotiation of United States–Mexico–Canada Agreement (USMCA) on the Agricultural Exports of Sinaloa State of Mexico
Bibliographic record
Abstract
Purpose: Mexico, like other countries, invested in measures to attract foreign direct investment to its territories. It, therefore, signed the North American Free Trade Agreement (NAFTA) in 1994, a treaty that facilitated Mexico to be the largest direct exporter to the United States. However, in 2018 the agreement was renegotiated and replaced with United States–Mexico–Canada Agreement (USMCA). This research is carried out to determine the advantages and disadvantages of renegotiation for Sinaloa's agricultural exports, with the question of whether it would negatively impact the Sinaloa's agricultural exports. Methods: The study focuses on the impact of renegotiation of the NAFTA on agricultural exports of the state of Sinaloa with indicators such as the Exports-Trade, GDP, and GDP Per capita of Mexico, opening to new markets, and logistics. Results: The renegotiation has a direct relationship with agricultural production in Sinaloa, with a serious negative effect, since overproduction would be created if the new destination for exporting from Sinaloa was not quickly available. Implications: This research can be of much use to the main agricultural exporting companies in Sinaloa, government agencies, and the Sinaloa Chambers of Commerce for decision making and policy formulation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".