FarmChat
Bibliographic record
Abstract
Farmers constitute 54.6% of the Indian population, but earn only 13.9% of the national GDP. This gross mismatch can be alleviated by improving farmers' access to information and expert advice (e.g., knowing which seeds to sow and how to treat pests can significantly impact yield). In this paper, we report our experience of designing a conversational agent, called FarmChat, to meet the information needs of farmers in rural India. We conducted an evaluative study with 34 farmers near Ranchi in India, focusing on assessing the usability of the system, acceptability of the information provided, and understanding the user population's unique preferences, needs, and challenges in using the technology. We performed a comparative study with two different modalities: audio-only and audio+text. Our results provide a detailed understanding on how literacy level, digital literacy, and other factors impact users' preferences for the interaction modality. We found that a conversational agent has the potential to effectively meet the information needs of farmers at scale. More broadly, our results could inform future work on designing conversational agents for user populations with limited literacy and technology experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.221 | 0.104 |
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".