The unspoken value of water infrastructure
Bibliographic record
Abstract
Water infrastructure forms the backbone of development, being pivotal for water, food and energy security. Both existing and new infrastructure must cope with global climatic challenges and increased human activity. Continuous investment in water infrastructure is crucial, yet in many cases, investments are deferred as they are not perceived as a priority, leading to deterioration, and public attention typically only arises after accidents or malfunctions occur. A prevailing lack of social awareness, combined with the mismatch between infrastructure lifespan and political cycles, further limits political will—especially regarding investment in ageing systems. This article was prepared to accentuate the extraordinary value provided by water infrastructure. Examples of recent global events are used to exhibit the profound benefits that rarely make their way into traditional cost-benefit analyses to inform decision making. These examples also showcase how essential sustainable development activities (SDG 6, 7, 9) would be severely compromised in their absence. This perspective also contributes to the ongoing debate about water infrastructure not being “fit for finance”, arguing that current financing and investment frameworks –as well as public perception– fail to capture the true societal and macroeconomic value of such systems, thus reinforcing their importance amidst changing climatic and human pressures.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".