Farmers’ Academy: A Novel Farmers-Driven Strategy for Agricultural Education, Research and Extension
Bibliographic record
Abstract
The idea behind this article revolves around the fact that while we are facing new critical challenges in agriculture, the farm diaspora must adopt adequate and timely measures to achieve the five securities: food, health, nutrition, energy and environment security. In this changing scenario, the farmers should play the central role in framing and implementing the policies of agricultural education, research and extension, and therefore the present agricultural system necessitates an overhauling transformation. We propose in this article a novel and realistic concept of ‘Farmers’ Academy’ for this purpose and delineate the rationale and genesis of this concept. This article deliberates on the existing agricultural system of education, research and extension, with special emphasis on the common approaches of extension, particularly by drawing examples from India, and suggests the potential role of Farmers’ Academy as a realistic approach with the fundamental idea “of the farmers, by the farmers and for the farmers”. The proposed structural and functional dimensions of a Farmers’ Academy including its day-to-day activities, organizing capacity building programmes, adopting climate smart agriculture, marketing and value chain activities, prospects of publication in information dissemination, launching of a website, role in strengthening research and development and in development of curriculum have been described. The requirement, strategy and benefit of convergence of farmer producer organizations with a Farmers’ Academy have also been discussed. The conceptualization of the concept of Farmers’ Academy by the first author and its successful implementation in Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India in 2015 have been recorded. The probable challenges and their possible way outs have also been depicted.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".