Success and Success Factors of Domestic Rainwater Harvesting Projects in the Caribbean
Bibliographic record
Abstract
<p style="margin: 0cm 0cm 10pt; text-align: justify;">In the Caribbean, domestic rainwater harvesting (DRWH) projects are being implemented to augment water supplies in water scarce islands and as a no-regret approach to adaptation to climate change. The evaluation of these projects is usually limited to the implementation process i.e. measuring the ability of the project to meet the set deliverables. Factors that are considered are the cost and time specified for the installation of the DRWH systems and the quality of the harvested water. There is seldom a post-project evaluation to determine whether the beneficiaries are able to properly maintain the system and or to improve on it, or whether the project is leading to increased household collection and use of rainwater in the project location and its environs. This paper is based on a survey of key stakeholders actively involved in the promotion of DRWH over a number of years. Active involvement was the basis of accepting the information on their perception as adequate in providing a reliable measure of the level of success of DRWH projects. The metrics for success were based on stakeholders’ perspective of the success of DRWH projects as determined by community involvement, rate of uptake of DRWH, increased awareness, impact of training on maintenance of systems, appropriate use of the systems, increased use of rainwater, increased capacity of community leaders to train and improved support by local private sector. It was found that there was willingness to invest in DRWH particularly among the stakeholders who have regularly used rainwater. The stakeholders were also asked to corroborate a set of pre-selected factors that were considered important for the successful development of DRWH projects. A ranking of these factors indicated that although the cost of the DRWH systems was the most important factor for success, technical issues were imperceptibly more important than economic and social issues.</p>
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".