Bibliographic record
Abstract
Domestic rainwater harvesting (DRWH), an old technology, is playing a key role in meeting some objectives of the UN “2030 Agenda for Sustainable Development” and building resilience to climate change, particularly in the Caribbean. DRWH projects can be implemented through self-financing, government subsidies, and micro-financing or by external agencies. Most recent promotion initiatives of DRWH have emphasized funding by external agencies, often ignoring the potential financial contributions of beneficiaries. Regional experiences have shown that, generally, the high initial capital costs for DRWH systems is a major constraint. However, in some cases, success in DRWH is possible through self-financing. This study reviews the experiences of some DRWH projects or by external agencies to determine a suitable financing mechanism. This paper shows that households can self-finance DRWH systems if payments are based on 5% of household income and interest rates are less than 5%, It concludes that the product/business cycle pattern of development adequately describes the development of DRWH in some parts of the Caribbean. It is recommended that such a model should be considered in designing DRWH projects through strategic partnerships of the beneficiaries with between local and international NGOs, community based organisations and domestic financial institutions like credit unions.
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".