Livestock Innovation System: Reinventing public research and extension system in India
Bibliographic record
Abstract
Innovations in livestock sector are needed as in any other sector, thus, continuously generated globally to increase livestock productivity and improve food security. Most of the research results, research and development outcomes and recommended innovations concerning livestock sector, however, remained confined to the laboratories and libraries in many countries including India. Hence, for effective generation and transfer of innovations, there is a need to gear-up the linkages between technology generation (research), technology dissemination (extension), technology users (farmers') and support mechanisms (inputs supply, market credit etc) and form a networking system among all the stakeholders leading to innovation system, which is dynamic in nature. To this end, the authors have focused on the concept of Livestock Innovation System (LIS) on the lines of Agricultural Innovation System (AIS), emphasizing on the idea that innovations come from multi-stakeholders like researchers, practitioners, innovative farmers etc. Among various multi-stakeholders, public research and extension system is a major policy instrument for promoting generation and transfer of innovations in majority of the countries in the world including India. This paper has reviewed public research and extension system in India in an effort to make a case for LIS with lessons drawn from select developed countries. It highlights the existing livestock research and extension system, challenges faced and future strategies for improving livestock productivity in India.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".