Digital technologies and food security during crisis: COVID-19 experiences from smallholder farmers in Odisha, India
Bibliographic record
Abstract
This research explores the significance of digital agricultural technology concerning food security for smallholder farmers and their communities in Odisha, India, amidst the COVID-19 pandemic. The study is a collaborative effort between academic researchers based in Canada, 15 small-scale farmers from Odisha, and the India-based social enterprise, eKutir. The farmers’ experiences utilizing eKutir’s technology are examined to understand how it aids them in meeting their food security requirements, sustaining their livelihoods and participating in local food systems before and during the pandemic. eKutir shares transactional data that illustrate how their platform facilitates food movement through local food systems during the pandemic while exploring its potential to enhance food security and food system resilience further. The primary goal of this research is to strengthen the adaptive capacity of smallholder farmers and the food systems dependent on them, particularly during non-conflict crises like the COVID pandemic, that affect economic, physical, and social aspects of life. This research is also intended to contribute to the continued development of digital technology in a way that supports smallholder farmer interests. Our findings reveal that access to eKutir’s platform enabled farmers to remain connected to essential markets, continue earning income, and support local food systems during the COVID pandemic. While potential negative consequences associated with data-focused agricultural technologies can acrue, when technology design is guided by and tailored to the needs of resource-constrained farmers, it can substantially contribute to their resilience in the face of contemporary challenges and risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".