Impact of Small Entrepreneurship on Sustainable Livelihood Assets of Rural Poor Women in Bangladesh
Bibliographic record
Abstract
The present study deals with the impact of small scale agricultural entrepreneurship on livelihood assets rural poor women and role of NGOs to developed women living of standard. The sample of the study consisted 300 women entrepreneurs those are involvement with livestock and poultry, fisheries, and vegetables entrepreneurship. Stratified Random sampling technique was used to obtained sample size. The study used the sustainable livelihood analysis framework as an analytical tool to identify ways to advance the livelihood of small entrepreneurship. Tobit and ordered probit regression estimation were used to analyze the result. Livestock and poultry entrepreneurship is significant and positively associated with financial capital, physical and social capital, vegetables entrepreneurship is significant and positively associated with natural capital and physical capital, fisheries entrepreneurship also positive and significantly associated with human capital. Role of NGOs micro credit and institutional support has great impact on women entrepreneurs living of standard. The analysis shows how entrepreneurs can achieve sustainable livelihood through access to a range of livelihood assets. Livestock and poultry entrepreneurs potentially provide higher economic returns, physical and social benefits. However, lack of resources, vulnerability and poor institutional support are identified as constraints to long term sustainability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".