Bibliographic record
Abstract
While the competitiveness of the Sri Lanka’s tea is declining in the global market, it is very important for Sri Lankan tea to evidently identify the reasons for declining competitiveness and how Sri Lanka can face this challenge fulfilling the demand of global market. The Sri Lankan tea industry has lost its market leadership position in the global market. With declining production, increasing cost of production, low farm productivity and price competition in the international market, Sri Lankan tea industry has lost its competitive advantage. Secondary data and primary data have been used for this study. 53 interviews have been conducted for this study in Sri Lanka and in India. Despite the fact that Sri Lanka is one of the major producers of tea, the local tea industry does not earn enough to be viable. Global consumers are paying more than ten times the price received by the Sri Lankan producers. The value addition is taking place in the consuming countries and the economic benefits of higher price for value added tea products go to the consuming countries. In this context the viability of the Sri Lankan tea industry makes it imperative to adopt production of value-added tea products, promoting local brands in the global market and marketing the products in the international market. The government should also provide further supports to this tea industry to be uplifted in the country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".