Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it