Brazilian pork meat competitiveness comparing to the main worldwide exporters (1990-2012)
Bibliographic record
Abstract
This work has the main objective of assess the competitiveness of pork meat exports from Brazil and its most important competitors on the international market (Germany, United States, Denmark and Canada), from 1990 to 2012. To this end, the Constant Market Share (CMS) method was used, comprising three effects: world trade; destination; and competitiveness. In order to better capture the changes in exports during the time, five sub periods were chosen, 1990/1993, 1994/1998, 1999/2002, 2003/2008 and 2009/2012. The analysis has demonstrated that United States are the most competitive country during the analyzed period, followed by Brazil. Although United States are more competitive, the growth percentage from Brazil was higher, 4,449.53%, while United States had 2,055.96% of growth. For both countries, ninety percent of this growth occurred due to increasing competitiveness. Denmark was the second country in pork meat exports in 1990, while Brazil and United States were not even among the ten larger exporters. However, Denmark presented the lowest percentage growth and the main reason was the decline of the competitiveness, not showing any positive competitiveness effect in the sub periods. Despite of been less competitive than the United States and Brazil, Germany is by now the largest exporter, with 704.45% of growth. Competitiveness was key to this growth in the complete period, but the increase in the imports of its most important importers was more relevant than in the other analyzed countries. Canada was the only country were most part of the exports growth was due to world trade growth of this product. The country also outstands because of its negative growing tendency at its main trade markets, especially the United States, which has become one of the larger players of world pig farming.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".