Bibliographic record
Abstract
Aims: The study aims to identify the coconut export market of Sri Lanka based on market share and market growth and to classify coconut-importing countries using BCG matrix in order to facilitate potential strategic marketing decisions.
 Study Design: This is a quantitative study based on secondary data.
 Place and Duration of Study: This study is based on Sri Lanka’s coconut exporting sector. The secondary data were collected from 2009 to 2019 from the various annual report of Central Bank of Sri Lanka, export performance report of Export Development Board of Sri Lanka and TRADEMAP.
 Methodology: The data were first tabulated and then generated as graphs to display market share and growth. The Boston Consulting Group (BCG) matrix was used to classify coconut export market into four groups, namely stars, cash cows, question marks and dogs.
 Results: Export performance of coconut sector increased based on the export value. Similarly, the percentage share of coconut exports to the total exports increased from 3.5% to 5.5% over the last decade. United States of America (USA), Germany and the United Kingdom (UK) are significant importers of Sri Lankan coconut and coconut-based products. India, Mexico and Australia showed a growing trend in the growth of market share for coconut and coconut-based products. According to the BCG matrix, the USA is categorised under the star market, and no countries fall in the cash cow market. Sri Lankan coconut market with India, Mexico, Australia, Germany, France, Netherlands, United Arab Emirates (UAE), UK, South Korea, Italy, Canada, Japan, China, Spain and Russia fall into question mark markets. Turkey, Pakistan, Egypt and Iran are grouped into dogs market category.
 Conclusion: By identifying the position of the country at the BCG matrix, the coconut industry would carry out activities and projects to earn additional income and capture more world market share for coconut and coconut-based products. Policymakers should consider the position of the country while implementing related policies.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".