International market concentration of fresh blueberries in the period 2001—2020
Bibliographic record
Abstract
Abstract There were no studies about the structure of the international fresh blueberry market in developing countries for contributing to the development of strategies and policies for the production, imports, and exports of fresh blueberries in the involved countries. The purpose of the study was to evaluate the structure of the international fresh blueberry market in the period 2001–2020. The research design was non-experimental, and longitudinal, with trends on per capita consumption, the market concentration index, and a multiple linear regression model. It was concluded that per capita consumption is led by Canada and the USA and that the concentration indices of the four main countries [CR(4)] of production, imports, and exports went from very high concentration levels to high concentration levels. The eight main countries [CR(8)] of production and exports were at a very high level and imports went from a very high level to a high level; in addition, the Herfindal–Hirschman-Index (HHI) of production was at a highly concentrated level: (a) highly concentrated level in imports in the period 2001–2018, (b) moderately concentrated from 2019 in imports, (c) highly concentrated in exports in the period 2001–2009, (d) moderately concentrated in exports in the period 2010–2018, and (e) not concentrated in exports as of 2019; in addition, the multiple linear regression model showed that per capita consumption, market share, price, and production contribute with 94.3% of the explanation of the variability of fresh blueberry exports. Finally, it was recommended to study the blueberry consumption habits and access restrictions to other international markets for increasing blueberry exports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".