Weaving indigenous agricultural knowledge with formal education to enhance community food security: school competition as a pedagogical space in rural Anchetty, India
Bibliographic record
Abstract
Like many socially and economically disadvantaged farming communities around the world, the Anchetty region of Tamil Nadu, India, has been experiencing serious food security challenges mainly due to the loss of traditional foods such as small millets and associated crops (SMAC) and associated indigenous agricultural knowledge (IAK). Drawing on community-based participatory research conducted in Anchetty’s Pandurangdoddy village, this paper explores the local understanding of IAK related to SMAC through young learners (school-going students) and their mentors (local farmers and community members), using a case study of school-based competition. Follow-up interviews with participating students, mentors and teachers were organised to explore the potential of a school competition as a pedagogical strategy to promote learning of IAK in formal school settings in order to safeguard the existing and future food security of local communities. There was a general consensus among the teachers, participating students, mentors (community members) and NGOs anout the potential for a school competition to create an alternative pedagogical space where IAK and curriculum-based knowledge could be intertwined and exchanged. Pedagogical spaces that weave IAK into schools, however, bring together the different and contested perspectives of the participants to understandings of the potential values of IAK.
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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 | 0.002 | 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 | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| 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".