Analysis of the chain of the banana industry of Ecuador and the European market
Bibliographic record
Abstract
Bananas are among the four main crops in the world, including wheat, rice, and corn. It is the most exported fresh fruit in the world in terms of volume and value. The European Union (EU) is the largest banana importer globally with an estimated volume share of 33%. Ecuador is the top exporter since it is responsible for one-quarter of the world banana exportation. It represents 22% of total world exports, 27% of total agricultural exports in the country and 8% of the value of all exports (including oil). The present work analyzed the chain of the banana industry of Ecuador and its position in the EU market. A non-experimental empirical method with a quantitative and qualitative approach was used supported by scholarly literature and secondary research data collection. Results obtained show that the main countries destination shipped 87.8 % of total bananas exported from Ecuador in the period of 2007 – 2017. The largest importer of Ecuadorian banana is the European Union (28.9%). In Ecuador, approximately 78% of the banana producers are small companies, by adding the medium ones 95.6% is reached. Thus, the production of bananas in the country is mainly based on the family economy. In 2019, the official banana box price for producers in Ecuador is USD 6.30. In the EU market, it can reach over USD 18.00. It suggested an unfair payment to small and medium producers. JEL CODE: Q13, M16, M21
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".