Determinants of South Africa’s Orange Trade Flows in the Top European and Asian Importing Countries
Bibliographic record
Abstract
South Africa orange exporters have for a long time enjoyed a sizeable market share in many parts of the world. A large portion of that sizeable market share can be located in the European countries then followed by fast developing countries in Asia. This market share can be associated with a declining South African currency compared to the values of these major currencies. On the other hand a number of trade agreements that have been reached by South Africa and these countries over the years have also contributed handsomely in the mentioned market share. Furthermore, diets of consumers in these countries have as well contributed in the conquered market share. Although a number of studies have been conducted on the subject of South Africa’s declining currency and the established trade agreements on products with mixed magnitudes in influencing trade flows, further research is needed for a better understanding on the trade determinants patterns in specific products. This paper focuses on the determinants of South Africa’s orange trade in the top European and Asian importing countries. In order to understand these trade determinants, gravity model has been applied to identify and analyze significant factors encouraging or discouraging the quantities/volumes of oranges exported to the above mentioned countries. Findings have shown that over the reviewed period, South Africa’s orange exports to the European market have been consistence, while exports to Asian market started slow and gradually increased over the years. Gravity model estimated coefficients also showed expected signs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".