Innovating at the margins: the System of Rice Intensification in India and transformative social innovation
Bibliographic record
Abstract
I explore transformative social innovation in agriculture through a particular case of agroecological innovation, the System of Rice Intensification (SRI) in India. Insights from social innovation theory that emphasize the roles of social movements and the reengagement of vulnerable populations in societal transformation can help reinstate the missing "social" dimension in current discourses on innovation in India. India has a rich and vibrant tradition of social innovation wherein vulnerable communities have engaged in collective experimentation. This is often missed in official or formal accounts. Social innovations such as SRI can help recreate these possibilities for change from outside the mainstream due to newer opportunities that networks present in the twentyfirst century. I show how local and international networks led by Civil Society Organizations have reinterpreted and reconstructed game-changing macrotrends in agriculture. This has enabled the articulation and translation of an alternative paradigm for sustainable transitions within agriculture from outside formal research channels. These social innovations, however, encounter stiff opposition from established actors in agricultural research systems. Newer heterogeneous networks, as witnessed in SRI, provide opportunities for researchers within hierarchical research systems to explore, experiment, and create newer norms of engagement with Civil Society Organizations and farmers. I emphasize valuing and embedding diversity of practices and institutions at an early stage to enable systems to be more resilient and adaptable in sustainable transitions.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".