Water on the mind: Mapping behavioral and psychological research on water security
Bibliographic record
Abstract
Abstract Water security as a concept recognizes the profound connections between the physical and social aspects of water. Yet, water security research features limited perspectives from two disciplines directly concerned with human behavior—the behavioral and psychological sciences. This review aims to characterize the main areas of research on water (including floods and droughts) which do feature concepts and methods from the behavioral and psychological sciences, discuss knowledge gaps, and draw attention to their potential to contribute to water‐related research. Bibliometric mapping of published water research identifies five research clusters and associated sub‐clusters: risk perception and flood, climate change and drought, water quality and water conservation, drinking water and bottled water, and mental health and WASH. A summary of research in each cluster and sub‐cluster highlights the application of many conceptual frameworks and behavioral determinants associated with water‐related behavior. Few articles focus on the role of governance or structural factors, and studies in low‐ and middle‐income countries are less represented in some clusters. The discussion considers the scope to apply higher level organizing frameworks for structuring behavioral and psychological science applications in water security and for exploring synergies with the physical and wider social sciences. In conclusion, further engagement with behavioral and psychological science within, between, and beyond the clusters identified here, could potentially deepen understanding of human–water interactions and enhance the design of measures to promote water security. This article is categorized under: Human Water > Water Governance Human Water > Water as Imagined and Represented Science of Water > Water and Environmental Change
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".