Empirical Analysis of Production and Productivity of Indian Spices
Bibliographic record
Abstract
The aim of this paper is to compare the growth rate in the production as well as productivity of the King and Queen of spices i.e., Black Pepper and Cardamom in two states, Kerala and Karnataka which are the leading producers of spices in India, the paper makes use of secondary data. the article highlights that the spice growers are switching their crops from pepper to cardamom and other plants. This can be caused by the reduction in productivity of pepper, and also in general, pepper cannot be grown as cardamom or paddy again on the same piece of land. Besides, the pepper price fluctuation is smaller than cardamom and other crops. However, the decrease in pepper productivity is above the degree of pepper price stability and this study highlights the plantation sector, more specifically, spices which contribute largely to the Indian economy, and which is often an underutilized sector despite its vast potential.
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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 | 0.000 | 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".