Design requirements for an online data exchange platform to bridge the gap between farmers and researchers in India
Bibliographic record
Abstract
The rising awareness of information transparency and the increasing trend of citizen participation in the agriculture sector has created new opportunities for information sharing. There are numerous information resources available for farmers from private, government sources and industry stakeholders. There are also various farm surveys by which farmers contribute towards the agricultural sector. However, no such platform connects farmers and researchers in which data exchange happens simultaneously between them. This gap in information exchange contributes to slow growth in the advancement of the agricultural sector. Research results do not reach the end-users in time to adopt agricultural improvement practices. Often researchers do not get the opportunity to engage and encourage farmers to be citizen scientists to contribute to the research. In this thesis, we develop design requirements for an online web-based prototype data exchange platform to bridge the gap between researchers and farmers. The platform can serve as a way to build farmers’ trust in researchers and encourage them to contribute more towards agricultural research to develop the sector. We believe that the findings of this study will prove helpful to interface designers and researchers to inform and guide future work in this critical area.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".