Bibliographic record
Abstract
Abstract As social scientists of water, we need to keep evaluating the analytical tools we use. Through the example of floods, we here develop a critique of one of those tools, the hydrosocial cycle framework, in order to expand our conceptualizations of water. The hydrosocial cycle is a re‐elaboration of the classical hydrological cycle which explicitly politicizes and denaturalizes the study of hydrological systems. In political ecology, such a conceptual framework has been pivotal to the understanding of how water circulates in society through a complex web of power relations, economic structures, and processes that are at the same time spatial and historical. But when we deploy this concept to examine floods, a number of limitations emerge. In this article, we formulate three specific theses which focus on those limitations: (a) an overemphasis on society, (b) a lack of attention to ecology and, more generally the relationships between water and other nonhuman elements and processes, and (c) a heuristic overreliance on the metaphors of flow and cycle. In developing these three theses, we discern alternative paths of analysis to conceptualize floodwaters at a time when these events increasingly constitute a significant threat to humans and nonhumans alike. Our hope is that this critique will also contribute to broader interdisciplinary debates about water and society. This article is categorized under: Human Water > Water as Imagined and Represented Water and Life > Conservation, Management, and Awareness Human Water > 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".