Co-creating water knowledge: a community perspective
Bibliographic record
Abstract
Navigating the complexities of global and local water resources challenges requires collaboration and mutual learning among diverse knowledge systems and disciplines. However, Western philosophical approaches to generating knowledge have prevailed in water management and hydrology, often overlooking community priorities, practices and perspectives, and power asymmetries - including gender inequalities, racism, and colonial injustices. In this perspective paper, we explore the co-creation of water knowledge (CCWK) concept to value multiple and diverse forms of knowledge. We identify four overarching principles (inclusivity, openness, legitimacy, and actionability), highlighting the importance of establishing relationships and collaborative leadership, adopting key tools and techniques, and integrating knowledge for water resources management. Furthermore, we argue that prioritizing epistemic justice is essential for effective CCWK. To address these, we advocate for more interdisciplinary and reflexive research practices that challenge and disrupt Western scientific traditions shaped by functionalist and colonial legacies.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".