Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sustainable Development Goal 6 aims to achieve universal access to water and sanitation services by 2030; this is expected to cost an estimated US$150 billion per year. Where will this funding come from? One possibility is private finance in the form of direct equity investment from private water companies and lending from commercial banks. Evidence suggests, however, that private investments in water and sanitation have not materialised as planned due to the sector's risk - return profile. Water and sanitation are considered ‘too risky’ by private investors and returns insufficiently rewarding. One alternative that may help to fill the water supply and sanitation (WSS) funding gap is an as yet untapped source of public finance: public banks. There are over 900 public banks in the world, with US$49 trillion in assets; they have, however, been largely underestimated as an important source of water and sanitation funding and have also been neglected by academic research and by mainstream policy organisations such as the World Bank. There is a need to better understand how public banks can be mobilised as effective funders of public water. In this article we provide a brief history of public banking practices in the water sector, review their pros and cons, and discuss the significance of the emergence of a new type of public water operator and the potential these entities offer for financing in this sector.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it