Dynamism and competitiveness of mexican beef, a focus of market diversification
Bibliographic record
Abstract
In Mexico, the production and export of bovine have remained competitive, placing it within the top ten countries due to its competitiveness which is based on weather conditions, the production of cereals for food and the commercial relationship with the United States. In this regard, Mexico, with the signing of the North American Free Trade Agreement (NAFTA), promotes international trade with the United States and Canada, with the objective of eliminating obstacles to trade, facilitating the cross-border circulation of goods and services, promoting fair competition in the free trade zone, increases investment opportunities, among others aims (Ministry of Economy, 1993). At the beginning of the agreement, Mexico achieved short-term macroeconomic objectives of stability, economic growth, and inflation control, however, in the long term it has not meant an increase in the standard of living of Mexicans, therefore, Mexico decided to diversify markets of export through bilateral trade agreements with other countries. As a consequence, the objective of this research is to perform an analysis of the commercial position of Mexican beef (2002-2016), obtaining the Grubel & Lloyd index and the grown rate by time interval, concluding that there is a commercial dynamism of this Mexican product.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".