MétaCan
Menu
Back to cohort
Record W6968450510 · doi:10.5281/zenodo.2573706

A STUDY ON THE ROLE OF REGIONAL RURAL BANKS IN PROMOTING SELF HELP GROUPS

2019· article· en· W6968450510 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBanking Sector Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Rural areaRural electrificationNoticeCensusRural economicsElectricityQuality (philosophy)Quarter (Canadian coin)

Abstract

fetched live from OpenAlex

Regional Rural Banks were created with the prime intention of providing banking and financial services to people living in rural areas. As per 2011 census report, rural India accommodates 833 million population, constituting almost 69% of the total population. Despite the fact that many developmental programmes initiated by the government from almost seven decades, have brought about considerable changes in the rural infrastructure and rural lives, a lot more to be achieved. It is pathetic to notice that, even now, many villages lacks basic infrastructural facilities. For instance, Department of Power, Government of India, in it’s report published on 31st March 2011, states that, out of 6,40,268 villages in the country, 5,39,163 villages have been provided with electricity connection so far. It constitutes 84% of the villages. This information narrates that, still 16% of the villages i.e., 1,01,105 villages doesnot have electricity connection at all. This means that about 400 million people in rural areas face the challenge of access to electricity and modern energy services. This has adversely affected the economic productivity of the people, forcing them to rely on unsustainable use of available bio-mass resources. The energy situation in rural India, further gets aggravated by poor quality fuels, unreliable supply, inefficient use of available sources and limited access to reliable sources of electricity. This information is only a tip of the ice berg of the numerous problems which are persistent in the rural set up. Availability of loans and other financial services was very difficult and next to impossibility. To address this issue government of India has nationalized 14 commercial banks in 1969. The nationalization of the banks in 1969 boosted the confidence of the public in the Banking system of the country. However, in the early 1970s, there was a strong feeling that even after nationalization, there were many cultural issues, which made it difficult for commercial banks, even under government ownership, to lend to farmers and the priority sector. This issue was taken up by the government and it set up Narasimham Working Group in 1975. On the basis of this committee’s recommendations, a Regional Rural Banks Ordinance was promulgated in September 1975, which was replaced by the Regional Rural Banks Act 1976 to provide sufficient banking and credit facility for agriculture and other rural sectors. As a result the development process of RRB’s started on 2nd October 1975 with the forming of the first RRB in the country, the Prathama Bank with authorised capital of Rs.5 crore at its starting. Also on 2nd October 1975, five regional rural banks were set up with a total authorised capital Rs.100 crore ($10 Million) which later augmented to 500 crore ($50 million). The Regional Rural Banks were owned by the Central Government, the State Government and the Sponsor Bank (Any commercial bank can sponsor the regional rural banks) who held shares in the ratios of Central Government : 50%, State Government : 15% and Sponsor Banks : 35%.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.022
GPT teacher head0.210
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicBanking Sector Performance and ManagementFrench-language works237,207