Myanmar Rice and Pulses : Farm Production Economics and Value Chain Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Agriculture continues to play a very \n important role in Myanmar’s economy. For many years, \n understanding the dynamics and performance of Myanmar’s \n agriculture has been difficult due to the absence of \n reliable, up-to-date data, at sectoral, sub-sectoral, or \n microeconomic level. During the past five years, significant \n changes have occurred in Myanmar’s demographics, economy, \n and public spending and in its integration into world and \n regional markets for agro-food products. While Myanmar’s \n agriculture has experienced some considerable \n diversification over the past decade, rice, and bean or \n pulses remain core elements of the sector. Rice remains an \n important crop and commodity for the economy and welfare of \n Myanmar. Myanmar’s paddy production has realized modest \n gains, yet it continues to under-perform, relative to peers \n and to its potential. One positive development at the \n production level has been a significant increase in labor \n productivity. One potentially disturbing trend has been a \n significant increase in agro-chemicals use in paddy \n production. Elsewhere in the rice value chain, many \n functions are characterized by low levels of operational \n efficiency and/or inadequate quality management. Myanmar is \n the world’s third largest producer of pulses, after India \n and Canada. Myanmar is also a major exporter of pulses \n globally and the largest in the ASEAN region. After several \n years of promising trade results, the pulses sub-sector \n experienced major problems in 2017 following India’s \n imposition of import restrictions on back gram, chick peas \n and other commodities. While the trade restrictions have \n exposed the vulnerability of the pulses sector due to its \n heavy reliance on a single market for some products, there \n are additional challenges faced by the sector. Elsewhere in \n the pulses value chain, there has been limited investment or \n value addition.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it