Global Water Governance and Climate Change: Identifying Innovative Arrangements for Adaptive Transformation
Bibliographic record
Abstract
A convoluted network of different water governance systems exists around the world. Collectively, these systems provide insight into how to build sustainable regimes of water use and management. We argue that the challenge is not to make the system less convoluted, but rather to support positive and promising trends in governance, creating a vision for future environmental outcomes. In this paper, we analyse nine water case studies from around the world to help identify potential ‘innovative arrangements’ for addressing existing dilemmas. We argue that such arrangements can be used as a catalyst for crafting new global water governance futures. The nine case studies were selected for their diversity in terms of location, scale and water dilemma, and through an examination of their contexts, structures and processes we identify key themes to consider in the milieu of adaptive transformation. These themes include the importance of acknowledging socio-ecological entanglements, understanding the political dimensions of environmental dilemmas, the recognition of different constructions of the dillema, and the importance of democratized processes.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Case analysis of global water governance arrangements; the object is environmental governance, not research governance.
The study analyzes global water-governance arrangements and environmental dilemmas.
Global water governance and adaptive arrangements; environmental governance, not research as object.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".