Grassroots Learning and Innovation in Award-Winning Agrotourism Community Enterprises in Thailand
Bibliographic record
Abstract
Innovative success in community-based agritourism often hinges not only on creative ideas but also on how community members learn, share knowledge, and build entrepreneurial capacity. This qualitative study investigates 11 award-winning Agrotourism Community Enterprises (ACEs) across Thailand, focusing on the educational processes that underpin their innovative practices. Data were collected through in-depth interviews, site observations, and document analysis, emphasizing nonformal and informal entrepreneurship education—such as community-based knowledge sharing, experiential learning-by-doing, and local mentorship. The findings reveal that these ACEs engage in a rich tapestry of grassroots learning activities: farmers and community entrepreneurs learn experientially through running homestays and farm tours, informally mentor one another in developing new products, and participate in nonformal training workshops facilitated by government extension programs and NGOs. These learning processes have enabled continuous innovation, from cultural heritage tourism and organic farming techniques to sustainable resource management and human resource development initiatives. Drawing on experiential learning theory (Kolb), community-based learning principles, and transformative learning theory, the discussion illustrates how iterative cycles of experience and reflection lead to new entrepreneurial ideas, how shared learning in the community fosters collective innovation capacity, and how these processes transform individuals and empower communities. The ACEs’ innovations are thus not only economic or environmental but deeply educational—strengthening entrepreneurial capacity, sustaining innovation, and fostering community empowerment from within. The study contributes to understanding how nonformal and informal education in rural communities can drive sustainable entrepreneurship. It concludes with recommendations for integrating experiential and community-based learning in rural enterprise development policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".