Digitalization of Agriculture in India: Advocating for Doubling Farmers' Income
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
The COVID-19 pandemic has underscored the necessity for a more resilient, efficient, productive, lucrative, and sustainable agricultural sector. Consequently, the Government of India has prioritized the optimal utilization of advanced technologies to ensure uninterrupted food security and empower farmers by doubling their income. This paper delves into the digitization of Indian agriculture to add value to the farming community and expand opportunities for doubling farmers' income. It examines the application of various digital technologies aimed at increasing farm yield, enhancing farm-level decision-making, optimizing resource utilization, and ultimately boosting the incomes of smallholder farmers. Utilizing an analytical approach, this paper draws insights from a survey of literature, relying on secondary sources such as books, research articles, policy documents, reports from government and non-government organizations, online databases, and discussion papers. The paper advocates for policymakers to concentrate on doubling farmers' income across different stages of food production and the supply chain.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it