Developing Novel Approaches to Tracking Domestic Water Demand Under Uncertainty—A Reflection on the “Up Scaling” of Social Science Approaches in the United Kingdom
Why this work is in the frame
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Bibliographic record
Abstract
Climate change, socio-demographic change and changing patterns of ordinary consumption are creating new and unpredictable pressures on urban water resources in the UK. While demand management is currently offered as a first option for managing supply/demand deficit, the uncertainties around demand and its’ potential trajectories are problematic for water resources research, planning and policy. In this article we review the ways in which particular branches of social science come together to offer a model of ‘distributed demand’ that helps explain these current and future uncertainties. We also identify potential strategies for tracking where the drivers of change for demand may lie. Rather than suggest an alternative ‘demand forecasting’ technique, we propose methodological approaches that ‘stretch out’ and ‘scale up’ proxy measures of demand to inform water resources planning and policy. These proxy measurements could act as ‘indictors of change’ to water demand at a population level that could then be used to inform research and policy strategies. We conclude by arguing for the need to recognise the co-production of demand futures and supply trajectories.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it