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Assessment of Effectiveness of Subsidized Food Grain Distribution in India with Respect to Rice and Wheat

2015· article· en· W2343677621 on OpenAlexaff
A. Amudhasurabi, Digvir S. Jayas, K. Alagusundaram

Bibliographic record

VenueIndian Journal of Marketing · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSubsidyDistribution (mathematics)Economic interventionismAgricultural economicsGovernment (linguistics)RevenueGovernment revenueBusinessScope (computer science)Public distribution systemAgricultureInflation (cosmology)Public policyPrivate sectorEconomicsFood securityMarket economyEconomic growthPoliticsGeographyFinance

Abstract

fetched live from OpenAlex

This paper assessed the rationale of public policy intervention in rice and wheat markets in India. Specifically, this study compared the government's economic cost for subsidized distribution of rice and wheat with the domestic market prices to examine the economic viability of the public distribution system. The statistical grouping of domestic market prices of rice and wheat displayed a significant inter-year variation in the recent years from 2007 to 2011. The price increase was much higher for rice than for wheat. The government's economic costs for distribution of rice and wheat through the public distribution system were close to the domestic prices. The paper presents a critical analysis of the government's policy on subsidized grain distribution. Suggestions on government policy and the role of the private sector are explained in the Indian scenario. Furthermore, possibilities for price stabilization in the private food grain market, control over food inflation, and scope for tax revenues for government from the private sector are also discussed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.247
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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