One Village One Product – rural development strategy in Asia: the case of OTOP in Thailand
Bibliographic record
Abstract
This paper investigates the development of the One Tambon, One Product (OTOP) programme in Thailand, based on the earlier experience of the One Village, One Product (OVOP) movement in Japan. Conceived in Japan as a policy to reverse rural depopulation, it has become more focused on poverty alleviation when implemented in Thailand. It will be shown how, despite some criticisms, the OTOP programme has been providing communities with the chance to market local output and creating employment opportunities. The paper includes a small sample survey of Chiang Mai province's OTOP and an enterprise case study. Résumé Cet article analyse le développement du concept « Un tambon, un produit en Thaïlande », basé sur le concept « Un village, un produit », crée au Japon comme une politique visée à renverser la dépopulation rurale. En Thaïlande, la politique a été conçue plutôt comme un moyen de réduire la pauvreté. Les auteurs démontrent comment le programme en Thaïlande, malgré certaines critiques, a réussi à créer les offres d'emploi et à stimuler les économies des communautés locales dans le pays. Cet article conclut avec une enquête sur la politique dans la province de Chiang Mai et un cas d'étude sur une entreprise locale.
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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 | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".