Now you see me, now you don’t: the role and relevance of paradigms in water governance
Bibliographic record
Abstract
Current understandings of water governance rely on a multitude of paradigms, defined as normative ideas collectively held by actor groups. These ideas shape how water challenges are framed and addressed; however, the ways in which paradigms influence governance processes and evolve across contexts remain underexplored. Reflecting on the role of paradigms in water governance enables a better understanding of the driving forces behind the implementation of certain water governance arrangements, their international spread, and what interests, politico-economic stakes or power dynamics are at play. This agenda-setting paper is a first attempt to bring together diverse insights on the role and functions of paradigms from various conceptual lenses to inspire more reflexive scholarly engagement with paradigms. Our approach is based on a four-year, iterative, interdisciplinary collaboration involving workshops and virtual labs with scholars from diverse backgrounds. From this process, we identify ten key agenda items for future research. These items highlight critical gaps and recommendations for scholars in the water governance field—such as the underexplored role of paradigms in shaping power relations, the neglect of contextual variation, and the marginalization of alternative epistemologies- which may also hold relevance for practitioners at times. Together, they provide both a conceptual foundation and practical direction for scholars and practitioners seeking to better understand and navigate the paradigm-driven dynamics of water governance.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.056 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".