Bibliographic record
Abstract
Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".