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Record W2188724002

Impact of Self-help Groups on Rural Women in Jammu District

2013· article· en· W2188724002 on OpenAlexaboutno aff
Poonam Parihar, Rakesh Nanda, S. K. Kher, Niaz Ahmed, Sukhvir Singh

Bibliographic record

VenueINTERNATIONAL JOURNAL OF COMMERCE AND BUSINESS MANAGEMENT · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofinancePovertyEconomic growthQuarter (Canadian coin)SocioeconomicsSelf-helpIndependence (probability theory)Rural districtBusinessSociologyGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Microfinance has evolved over the past quarter century across India into various operating forms and to a varying degree of success. One such form of microfinance has been the development of the self help movement. SHG is a group of rural poor who have volunteered to organize themselves into a group for eradication of poverty of the members. SHG is a group formed by the community women, which has specific number of members like 15 to 20. In such a group the poorest women would come together for emergency, disaster, social reasons, economic support to each other have ease of conversation, social interaction and economic interactions. This study on Impact of Self Help Groups on rural women was conducted to study the formation mechanism of Self Help Groups and to measure the impact of SHGs on rural women in means of decision making. The study consists of 250 women members of SHGs and 250 non- SHG women members as selected respondents. The findings indicate that highest majority of women participated in SHGs for economic independence, income-generation activities and social contacts. The impact of decision making on SHG members were found significantly higher than non- SHG members in Household Expenditure, Education of Children, Marriage of Children, Marketing and Social Customs in the home.

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)0.000
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 score0.210
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

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