Impact of Microcredit on Rural Poverty Alleviation in the Context of Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microcredit and poverty alleviation have become the two sides of a coin as the role of microcredit on poverty alleviation is well accepted in the arena of economic development. This study is an attempt to analyse the impact of microcredit on poverty alleviation in the rural areas namely Hathazari, Mirsharai and Sitakunda upazilla (sub-units of district ) of Chittagong district, Bangladesh. A cross sectional survey was conducted on the rural part of these three upazillas. Data have been collected through a well-structured questionnaire from 100 microcredit-recipients/borrowers of Bangladesh Rural Advancement Committee (BRAC) and Association for Social Advancement (ASA) - two giant microcredit providers in Bangladesh and from 50 non-borrowers of the study areas. Respondents were selected randomly. Tabular method was used to describe the data. Hypothetically, the outcomes were found significant resulted from chi-square test (X ² -test) and ANOVA (Analysis of Variance) without an exception for clothing expenditure. The study revealed that microcredit disbursed through BRAC and ASA, plays a dynamic role to reduce poverty in the study areas by income generating activities of the poor women borrowers and by improving their living standard. It is found from the study that microcredit has  positive impact on income, expenditure, condition of dwelling house, education, health and decision making ability of the poor women borrowers who spent at least five years in BRAC and ASA comparing to the non-borrowers.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it