Small millets-based livelihoods and actually existing markets in Andhra Pradesh, India
Bibliographic record
Abstract
The decline in cultivation and consumption of small millets crops seen across India in recent decades is a concern for many. These highly nutritious coarse grains hold significant cultural value as traditional foods for tribal farming populations and remain important contributors to regional agro-biodiversity. Born of out this concern, small millets have garnered recent attention as underutilized crops with potential to contribute to regional food and nutritional security through market development. By localizing small millets within the broader context of agricultural change, this work investigates links between cultivation, distribution and consumption – or the market chain – of small millet varieties in northern coastal Andhra Pradesh, India. Employing an interdisciplinary methodology drawing from anthropological and agribusiness approaches, this study conducts an in-depth, qualitative market chain analysis for finger millet and little millet varieties to produce a multi-sited ethnographic work on informal agricultural marketing in the case study area. In incorporating the political economic, historical and cultural dimensions of millets and other crops, this research teases out the complex relationships between food security, livelihoods, agricultural marketing and development interventions. This research aims to demonstrate how a holistic study of an agricultural commodity, which includes on-farm cultivation and consumption, can get at how smallholder farmers participate in local markets, in everyday practice, and how they engage with change. In connecting a traditional market chain analysis with detailed ethnographic study on the ground, we can see how farmers engage with markets embedded in particular historical and sociocultural contexts. Further, this work provides insights into the challenges of small millets-based livelihoods, going beyond the market to explore the many social institutions in which market participation is embedded. In doing so, I argue that nuanced approach to millets-based livelihoods, commercial crops and broader agrarian transition is necessary.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".