Bibliographic record
Abstract
Abstract. The world is rapidly urbanizing. One of the challenges associated with this growth will be to supply water to rapidly growing, developing-world cities. While there is a long history of interdisciplinary research in water resources management, relatively few water studies attempts to explain why water systems evolve the way they do; why some regions develop sustainable, secure well-functioning water systems while others do not and which feedbacks force the transition from one trajectory to the other. This paper attempts to tackle this question by examining the historical evolution of one city in Southern India. A key contribution of this paper is the co-evolutionary modelling approach adopted. The paper presents a "socio-hydrologic" model that simulates the feedbacks between the human, engineered and hydrologic system for Chennai, India over a forty year period and evaluates the implications for water security. This study offers some interesting insights on urban water security in developing country water systems. First, the Chennai case study argues that urban water security goes beyond piped water supply. When piped supply fails users first depend on their own wells. When the aquifer is depleted, a tanker market develops. When consumers are forced to purchase expensive tanker water, they are water insecure. Second, different initial conditions result in different water security trajectories. However, initial advantages in infrastructure are eroded if the utility's management is weak and it is unable to expand or maintain the piped system to keep up with growth. Both infrastructure and management decisions are necessary to achieving water security. Third, the effects of mismanagement do not manifest right away. Instead, in the manner of a "frog in a pot of boiling water", the system gradually deteriorates. The impacts of bad policy may not manifest till much later when the population has grown and a major multi-year drought hits.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".