Understanding rivers and their social relations: A critical step to advance environmental water management
Bibliographic record
Abstract
River flows connect people, places, and other forms of life, inspiring and sustaining diverse cultural beliefs, values, and ways of life. The concept of environmental flows provides a framework for improving understanding of relationships between river flows and people, and for supporting those that are mutually beneficial. Nevertheless, most approaches to determining environmental flows remain grounded in the biophysical sciences. The newly revised Brisbane Declaration and Global Action Agenda on Environmental Flows (2018) represents a new phase in environmental flow science and an opportunity to better consider the co-constitution of river flows, ecosystems, and society, and to more explicitly incorporate these relationships into river management. We synthesize understanding of relationships between people and rivers as conceived under the renewed definition of environmental flows. We present case studies from Honduras, India, Canada, New Zealand, and Australia that illustrate multidisciplinary, collaborative efforts where recognizing and meeting diverse flow needs of human populations was central to establishing environmental flow recommendations. We also review a small body of literature to highlight examples of the diversity and interdependencies of human-flow relationships-such as the linkages between river flow and human well-being, spiritual needs, cultural identity, and sense of place-that are typically overlooked when environmental flows are assessed and negotiated. Finally, we call for scientists and water managers to recognize the diversity of ways of knowing, relating to, and utilizing rivers, and to place this recognition at the center of future environmental flow assessments. This article is categorized under: Water and Life > Conservation, Management, and Awareness Human Water > Water Governance Human Water > Water as Imagined and Represented.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.020 | 0.038 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".