The System of High-Qualified Thung Kula Ronghai Jasmine Rice Management for Export to the People’s Republic of China
Bibliographic record
Abstract
The objectives of this qualitative research were to study the historical background of management of Thung Kula Ronghai Jasmine Rice for export to the People’s Republic of China; to study current circumstances and problems of management of Thung Kula Ronghai Jasmine Rice for export to the People’s Republic of China; and to study the system of high-qualified Thung Kula Ronghai Jasmine Rice management for export to the People’s Republic of China. The fieldwork data and documents were collected using a survey, observations, interviews, and focus-group discussions. One hundred and five informants were selected from Surin, Roi ET, and Si Sa Ket provinces. The analysis of data was based on the objectives of the research and done descriptively using a triangulation technique. The research found that the Jasmine Rice 105 was brought to the area by government agencies for commercial purposes. Currently, the Jasmine Rice businesses in Thung Kula Ronghai were operated by private rice mills and community business organizations and the rice export was in the hands of middlemen coming from outside and making product brands and packaging themselves. The management of local mills and businesses included paddy grains purchasing, reducing of moisture, storage, processing, and packaging. Although the management as a whole did not meet the international standards, the rice export to China should do the growing of rice, processing, and exporting systematically.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".