Understanding access to agrarian knowledge systems: Perspectives from rural Karnataka
Bibliographic record
Abstract
In this paper, we attempt to unpack the existing landscape of agricultural extension services and delve into questions of access to and localisation of knowledge to understand how these conditions (access and localisation) determine climate change adaptation in agriculture in the southern Indian state of Karnataka. Our empirical findings suggest that the current extension framework reproduces existing inequalities in that access to institutional knowledge and its uptake is linked to one’s social location, that is, caste, gender, class, and geographic location, and information shared is neither timely nor contextually relevant. Employing accessibility and localization as lenses of inquiry, we argue from empirical evidence that smallholder farmers in a rain-fed context are especially vulnerable to the risks posed by climatic change and hence agricultural extension (with climate-informed knowledge) should be to be seen as a critical enabler of adaptation; ensuring accessibility and localisation, we argue, strengthens climate services, and by extension, enables adaptation to climatic risks. The issues that encumber effective extension, we contend, can be mitigated by a re-imagination of agricultural extension, one that privileges public field level functionaries as conduits between state departments and farmers over other modes, and enables structured involvement of community collectives as vehicles to address local needs and ensure access. Drawing on interventions in our study sites, we make a case for promoting knowledge systems that ensure access to climate-specific agricultural information and contextual embeddedness.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".