Collaboration and coordination in the United Nations 2023 Water Conference commitments
Bibliographic record
Abstract
ABSTRACT The United Nations 2023 Water Conference brought together world leaders to commit to addressing water challenges and achieving water and sanitation for all. The conference resulted in the development of the Water Action Agenda (WAA), a collection of commitments from governments and organizations to address water issues. Achieving water security requires solutions that involve cross-sectoral coordination and collaboration between water and other resource governance sectors. Therefore, this research evaluates the collaboration and cross-sectoral coordination within the WAA through the lens of the water–energy–food (WEF) nexus to analyze the WAA commitments (n = 835). It quantitatively examines the amount of collaboration between organizations and the level of the multi-resource interconnections within the commitments. It then qualitatively analyzes how the WEF nexus is incorporated into the WAA. The results show that there is a high level of collaboration and multi-resource coordination across all the commitments and that the application of the WEF nexus includes increasing shifts from academic theorization toward implemented practice. However, limitations of the WAA are evident: there is limited accountability to ensure that commitments are fulfilled, and the commitments themselves may not represent the best actions to achieve water and sanitation for all.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".