Establishing Sustainable Food Production Communities of Practice: Nutrition Gardening and Pond Fish Farming in the Kolli Hills, India
Bibliographic record
Abstract
This study describes the formation of nutrition gardening and pond fish farming communities of practice (CoPs) among small-scale farmers of the Malayalis tribe living in the Kolli Hills region of Tamil Nadu, India. We examine the factors that have shaped the formation of these CoPs, their purpose and function, who is involved, what activities hold these communities together, and their role in strengthening sustainable food production and consumption practices. Data were obtained through participatory rural appraisals (PRAs), key stakeholder interviews, and participant observations during four months of fieldwork. The primary motivations that led the nutrition gardeners and pond fish farmers to become part of CoPs were to improve the health and nutrition of their families and to obtain expert advice in sustainable food production practices. Both CoPs are in the early stages of development and differ not only in the types of food they produce and the skills and tools needed for their success, but also in their structure; nutrition gardening takes place at the individual and/or household level, whereas pond fish farming operates at the group and/or community level. The ways in which members experience being in a community also differs. Nutrition gardeners rely on open-ended conversations and community creation through relationship building; in contrast, fish farmers find that group meetings and maintaining transparent record-keeping are most important. Sustainability of these practices and the CoPs depended on factors internal to the communities (e.g., leadership, knowledge mobilization) as well as external factors (e.g., rainfall and market potential). See the press release for this article.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".