Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.006 |
| 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 it