Mapping institutional landscapes: global efforts to improve access to water
Bibliographic record
Abstract
The Paul Simon Water for the Poor Act 2005, the UN Millennium Development Goals, and Water as a Human Right are all mechanisms for enhancing access to water for the world's poorest populations.However, these water policies are not integrated into a holistic framework.They are fragmented between multiple governing agencies, founded on competing ideologies for water management, and enforced through confusing regulatory structures for land tenure tenuously linked to water rights.Alternatively, this is the governing landscape that provides the basis for innovative approaches to water solutions: integrated water resource management (IWRM), collaborative partnerships, and adaptive management strategies focused on place-based solutions.This paper examines efforts to map the intersection of poverty and water focusing on access to water and sanitation.Webbased geospatial tools of global water access issues are reviewed.The US Paul Simon Water for the Poor Act 2005 (WfP Act), the human right to water and sanitation (General Assembly Resolution 64/292, 2010), the UN Millennium Development Goals (MDG) to improve access to water and sanitation (Goal 7), and the codifi cation of water as a human right into law by specifi c water poor countries are spatially cross-referenced to map the institutional landscape where water policy and water need intersect.Fundamental to improved access to water and sanitation is the need for a healthy environment.We conclude our research by examining the question: how well can these policies reconcile the confl icting demands upon the socio-ecological landscape?
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".