Bibliographic record
Abstract
‘Water wars’ are back. Conflicts in Syrian, Yemen and Israel/Palestine are regularly framed as motivated by water and presented as harbingers of a world to come. The return of ‘water wars’ rhetoric, long after its 1990s heyday, has been paralleled by an increasing interest among novelists in water as a cause of conflict. This literature has been under-explored in existing work in the Blue Humanities, while scholarship on cli-fi has focused on scenarios of too much water, rather than not enough. In this article I catalogue key features of what I call the ‘water wars novel’, surveying works by Paolo Bacigalupi, Sarnath Banerjee, Varda Burstyn, Assaf Gavron, Emmi Itäranta, Karen Jayes and Cameron Stracher, writing from the United States, India, Canada, Israel, Finland and South Africa. I identify the water wars novel as a distinctive and increasingly prominent mode of ‘cli-fi’ that reveals and obscures important dimensions of water crises of the past, present and future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".