Bibliographic record
Abstract
It is generally believed that the food processing sector can exercise market power on raw material producers and final consumers. The objective of this study was to assess the degree of oligopoly and oligopsony power of the tea-processing sector. A partial equilibrium model was developed for the world market for tea, treating India, Kenya and Sri Lanka as raw tea producers and Canada, United Kingdom and the United States of America as tea consumers. An imperfectly competitive tea-processing sector was incorporated in the model allowing conjectural variation elasticity to represent the degree of market power. The model was econometrically estimated using the two-stage least square estimation procedure. Results of the econometric estimation show that all the market power estimates are statistically significant. The conjectural elasticity values in the input market are 0.0516, 0.0015 and 0.1657 for India, Kenya and Sri Lanka respectively. The conjectural variation elasticity in the output market is 0.1273. The elasticity of supply with respect to own prices are 0.0791, 0.2268 and 0.2060 for India, Kenya and Sri Lanka respectively. The elasticity of demand with respect to own prices are –0.4720, –0.1556 and –0.1237 for Canada, United Kingdom and the United States respectively. The resulting Learner Index for Sri Lanka is very small indicating that Sri Lankan tea producers are not significantly exploited by tea processors. DOI: http://dx.doi.org/10.4038/sjae.v5i0.3477 SJAE 2003; 5(1): 69-82
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".