Social Capital and Development Failure The Case Study of a Sub surface Arsenic Removal System Site in Narail Bangladesh
Bibliographic record
Abstract
The role of social relationships in development has important implications for contemporary development research and policy.Projects in Bangladesh fail more frequently that they should and one reason may be due to a lack of social capital among locals.The SAR (Sub-surface Arsenic Removal) system is a very viable and sustainable way to produce arsenic-free fresh water in areas like rural Bangladesh.One such SAR system is being developed in Barnal-Eliasabad in Kalia upazila of Narail district, Khulna.The aim of this study is to show that the SAR water supply system is likely going to be unsuccessful due to a lack of social capital among the villagers of Barnal-Eliasabad, Kalia upazila.The study investigated respondents' insights on the causes of failure of the community based SAR project.Qualitative research method was used to understand the research problem better.The questionnaires and interviews revealed that the locals are very dissatisfied with the SAR system, and cited some issues.However, further investigation reveals that the key reason for the failure is due to internal strife among the major families.The lack of desire to work together in spite of their awareness of the dangers of arsenic poisoning means that regardless of the all the positives, the SAR system will inevitably fail due to a lack of local support and the village will continue to use contaminated and/or limited sources of freshwater.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".