Water scarcity: An alternative view and its implications for policy and capacity building
Bibliographic record
Abstract
This article focuses on the somewhat ambiguous concept of scarce water, or, more accurately stated, on the rather more ambiguous concept of scarcity. Still today, water scarcity in a region is defined largely in physical terms, typically gallons or cubic metres per capita if a stock or per capita‐year if a flow. However useful purely physical measures may be for broad comparisons, they cannot adequately reflect the variety of ways in which human beings use water — neither to their wastefulness when water is perceived as abundant nor to their ingenuity when it is not. This article argues that water scarcity should be defined according to three orders of scarcity that require, respectively, physical, economic and social adaptations. It goes on to demonstrate that perceiving scarcity mainly in physical terms limits opportunities for policy‐making and approaches for capacity building.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| 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".