Sustainability of Community Based Water Projects: Dynamics of Actors’ Power Relations
Bibliographic record
Abstract
Sustainability of Community Based Water Projects (CBWP) in Central Tanzania was examined. Actors’ power was hypothesized to influence sustainability of the CBWP. Power was analyzed from two aspects; power structures and power relations. A cross-sectional design was adopted, which allowed data to be collected once at a point. A multi-stage sampling technique was used to get the study sample. Stratified random sampling was employed to get 30 CBWP for the study and simple random sampling was used for obtaining 390 households. Purposive sampling was also employed to obtain the key informants for in-depth interviews and Focus Group Discussions Quantitative data obtained were processed and analyzed using Statistical Package for Social Sciences (SPSS 16v.) while qualitative data were transcribed and content analysis was employed in analyzing them. In examining the power structure the study shows that institutions and empowerment have significant correlation with sustainability status of the CBWP. The study also shows that global and national actors have high influence/power in effecting the CBWP critical activities. The local actors are perceived to be powerless; however, the study indicates importance of recognizing them because their position as powerless actors may limit their participation in the CBWP and thus jeopardizing the chances of yielding sustainable CBWP.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".