The National Food Security Act (NFSA) 2013: Challenges, buffer stocking and the way forward
Bibliographic record
Abstract
The National Food Security Act (NFSA) 2013 combines and expands the scope of some existing food-based welfare schemes. It will be distributing raw rations, meal(s) and/or cash. Approximately 81.35 crore persons or 16.57 crore households are to benefit under the targeted public distribution system (TPDS) under the Act. The annual food grain requirement is estimated at 61.43 million tonnes with annual food subsidy implication of around Rs. 1.31 lakh crore. The paper empirically maps the annual distribution commitment (61.43 MMTs) of the government with the procurement pattern of rice and wheat, for each quarter, to estimate the quarterly operational stocking norms. In addition to the 61.4 MMTs grains, needed to meet the operational needs, the country also stocks for strategic needs. The paper proposes creation of 10 MMTs of grains in this regard- five MMTs to be procured from the domestic market and the remainder from the international market on a need basis. By re-introducing the concept of fungibility between the operational and strategic stocks and by utilizing the dynamics of the procurement pattern, the paper shows that the 61.4 MMTs of annual grain procurement will be sufficient for both the operational and strategic stock needs of the country. The estimated new norms (Scenario 2) are January - 21 MMTs, April - 18.7 MMTs, July - 36.8 MMTs and October - 24 MMTs. Recently approved CCEA norms, on comparison, are found to be on the higher side indicating the government's implicit preference for lower risk (the government stocks higher levels of strategic reserves, used mainly to smoothen inter/intra year fluctuations, than required) even if that implies higher costs. There are wider apprehensions that the Act will fail to deliver on the promises made. The bigger operational challenges include- ensuring the adequate supply of grains every year, lowering per person entitlement or population coverage particularly when the population is expanding, unpreparedness of the implementing states, slowing down the natural process of agricultural diversification by increasing the relevance of rice and wheat in the system. Therefore, the immediate suggestion is not to hurry in the NFSA implementation process, especially not without satisfying its pre-conditions in each state. Explicit challenges that the continuation of the existing system pose on the system warrants one to devise an appropriate income policy instrument to substitute NFSA (...)
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".