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Record W2091128987 · doi:10.5539/jsd.v4n1p240

Sustainable Rural Development though Women Participation in SMEs Business Growth in Sindh

2011· article· en· W2091128987 on OpenAlexvenueno aff
Nanik Ram, Imamuddin Khoso, Shaukat Ali Raza, Kamran Shafiq, Faiz Muhammad Shaikh

Bibliographic record

VenueJournal of Sustainable Development · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSustainable developmentSustainable growth rateBusiness developmentWomen entrepreneursEconomic growthPolitical scienceMarketingEconomicsEntrepreneurshipLawFinance

Abstract

fetched live from OpenAlex

The present research paper is focused is focused on the sustainable development through women participation in SMEs business growth in Sindh. The main objective of current research is to examine the sustainable development through women participation in Small and medium enterprises in upper Sindh. The data were collected from 300 respondents from five Districts Dadu, Nawabshah, Shikarpur, Jacobabad and Kashmore district by using simple random technique. It was further revealed that the rural women is less confident and their husbands were always given them hard time once they are exposing themselves to out side the boundaries of the house. It was revealed the rural women is innovate designs of toppi (Sindhi Caps) as well as other SMEs products which are the only source of earning. They are also paid 60% less value of their products because of lack of marketing and other facilities. The biggest challenges which they were facing they were doing all business in house, lack of marketing facilities, Karo Kari criminal activities and they were deprived from the basic rights. This study contributes and explores the Rural Women challenges in SMEs business and how these critical unethical problems we can overcome like KARO KARI, and other various social issues growth.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.264
Teacher spread0.244 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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