What’s the Missing Link? - Reviewing Climate Change Polices in Context of Indian Agricultural Sector
Bibliographic record
Abstract
Government of India has recently announced for special funds like National adaptation fund to handle market risks arising due to climatic variability. The operational protocol of this fund and other forthcoming initiatives have yet to be expanded, but through this paper we like to draw attention to some of the policies and programmes that the Governments of India have already initiated and that directly or indirectly link to managing the risks and challenges faced with climate change in context of agriculture sector. These policies have their own merits and demerits, but it is the need of the hour to draw synergies between the existing polices and new proposed actions to draw on the strengths of the ongoing programs and build up on that. This paper is also relevant in the context of UN Climate summit 2014 held on 23rd September and Food and Agriculture Organisation (FAO) aim at a global alliance for climate smart agriculture along with India’s prime ministers speech on 15th august, 2015 and the budget speech emphasising on threat that agriculture is facing because of climate change and government willingness to give emphasis on this agenda in context of agricultural sector. With the critical analysis, we also want to highlight the richness in the policy framework of several policies which are interlinked with each other, but due to lack of coordination they implementation might not be very appropriate.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".