University-community Partnership Contribution Towards Rural Sustainability: Participatory Action Research in the Rice Farming Community of Paipayales, Ecuador
Bibliographic record
Abstract
Abstract Rice cultivation is the main economic livelihood for many families around the world. This activity represents several challenges for farmers and community members for rural sustainability, a cross-cutting element of the Sustainable Development Goals (SDGs) of the United Nations (UN). In response, the Polytechnic University (ESPOL), fulfilling its mission of linking with society, implemented a community program where students and professors interact and collaborate with rice farmers in the rural community of Paipayales, located in the Santa Lucia canton, Guayas province. This article explores the impact of university-community projects through the Participatory Action Research (PAR) approach in order to evaluate them as a tool for contributing towards rural sustainability in communities. As a result, it was determined that the main problems faced by most of the farmers of the “Dios con Nosotros” Association are the availability of water in the wells and the commercialization of paddy rice. Considering these problems, the wells were geolocated and a board was designed for proper water management; at the same time, water quality was studied and recommendations were presented according to the problems encountered. Two proposals were also presented to create a rice husker and a rice separator to increase their profit margin by selling rice directly to retailers and wholesalers. As relevant conclusions, the importance of implementing links and relationships between the university community and society was highlighted, guaranteeing the value of working in transdisciplinary teams and achieving a comprehensive intervention that would lead to significant improvements in the community.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".