Impact of Non-Government Organizations and Microcredit Institutions on the Development of Bangladesh
Bibliographic record
Abstract
In this paper, I will argue that although local microfinance institutions and non-governmental organisations such as BRAC and Grameen Bank have played a significant part in the development and reduction of poverty in Bangladesh, some challenges remain. This paper will first present a brief background of the socioeconomic conditions of Bangladesh. The history of NGOs in the country and their transformation from primarily relief oriented agencies to being full-scale development actors will also be explored. I will analyse the relationship between the state and the non governmental agencies in delivering service to the people of Bangladesh. Furthermore, this paper will present the rise of microcredit institutions and facilities that have changed the economic landscape of Bangladesh. Finally, the essay will present the challenges that are faced by non governmental agencies in the country. This paper will bring forward the bureaucratic hurdles and the political opposition that are faced by such agencies. Public perception of NGOs will also be highlighted in this essay. I will also present the criticisms that some of the microcredit organisations such as Grameen Bank have faced due to their high emphasis on repayment of loans.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".