Factors Influencing the Competitiveness of Coffee Growers in Puebla, Mexico, to Export to the United States
Bibliographic record
Abstract
This research aims to examine whether price, productivity, quality, innovation and technology transfer (TT), exchange rate, and financing are key factors that influence the export competitiveness of the coffee industry in the state of Puebla, Mexico. Previous studies on competitiveness have explored classical and neoclassical theories and new theories of international trade. We collected information by conducting an online survey using Google Forms, which included indicators for each of the variables studied. The multivariate regression model was employed to analyze the relationship between the dependent and independent variables, and we used the Statistical Package for the Social Sciences to process the data. The results indicated that the studied variables significantly affected coffee producers’ export competitiveness in Puebla. Specifically, the model’s exchange rate, quality, and productivity variables demonstrated considerable explanatory power. This underscored the importance of product quality, production volume, and factors related to the producer’s income in the decision to export coffee rather than sell it in the domestic market. Despite the emphasis placed on variables such as innovation and TT in the literature, they had limited relevance in the used model. However, we discussed several potential explanations for this outcome.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| 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".