A Critical Analysis of the Value Chain in the Rice Industry and Its Effects on the Export Rice Industry in Kien Giang Province, Vietnam
Bibliographic record
Abstract
The study of a critical analysis of the value chain in the rice industry and its effects on the export rice industry in Kien Giang province, Vietnam conducted during the period from December 2012 to November2015. The research result showed that there were 450 persons who are rice exporters and farmers (412 processed and 48 missed) who to be interviewed and answered nearly 27 questions. The researcher had analyzed KMO test, the result of KMO analysis used for multiple regression analysis. The person responses measured through an adapted questionnaire on a 5-point Likert scale. Hard copy and interviewrice exporters and farmersby questionnaire distributed among rice exporters and farmers in KienGiang province. The regression analysis results showed that there were seven factors, which included of factors following: Development strategy; Control policy; Planning; Support policy; Rice seeds;Cultivation techniques andPost-harvest processingactually affected the export rice industry with 5 % significance level. The main objectives of this study were to to conduct a survey to find value chain that affecting the export rice industry in KienGiang province, to identify value chain that affected on the export rice industry in KienGiang province and to analyze and to test value chain that affected the export rice industry in KienGiang province.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".