Linking Water Technologies with Water Practices: Case Studies from 11 Countries
Bibliographic record
Abstract
Abstract Radical transformations of technology development are required to meet the rapidly increasing global water resource challenges. A systemic understanding of the impacts of water technologies on water practices across countries remains limited. This paper aims to develop an understanding of the linkages between water technologies and water practices in the 11 countries where 95% of the world’s water patents were produced. The water technological development was assessed by both the contents (different types of technologies) and the structure (the technology network). The water practices were presented by country-level indicators representing their water demand, water supply, and water management collected from public databases. It was found that there was an extremely uneven distribution with the top 3 countries (China, Korea, Japan) accounting for over 70% of the total patents, and there were slow growth rates and even a decline of water technologies. All countries demonstrated homogeneous technological development focusing on water supply, and their networks had limited brokerage capacity for knowledge diffusion. Australia, China, Canada, France, and Korea had relatively good links between water technologies and practices, whereas the majority of countries demonstrated unbalanced technological structures with a de-linking to water practices. These findings can assist in developing water technologies for improving water practices in 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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".