Brazil’s Commitment to Train the Next Generation of Water Leaders: the Potential Role of UNESCO-Hidroex
Bibliographic record
Abstract
Brazil has made a strategic decision and formal commitment to the International Community to invest in developing a post-graduate water education center to help train the next generation of water leaders in Latin America and Portuguese-speaking Africa in response to the demand of the UNESCO Member States. The International Centre for Education, Capacity Building and Applied Water Research – Hidroex will, overtime, greatly augment the work of the UNESCO Institute for Water Education (UNESCO-IHE) in Delft, the Netherlands as a member-institution of the proposed UNESCO Global Campus for Water Education. This center of excellence will also develop a new institutional model in two regards: first by implementing the concept of a “water condominium” which will attract scientists from around the world sharing a common research interest providing logistical support including laboratories, analytical capacity, video conference facilities and offices – all with the aim of addressing complex, priority water research issues, and resulting in new networks of collaborating scientists, and secondly by integrating the goals of eighth phase of the UNESCO International Hydrological Program into the education and research programs of Hidroex to develop the environmental conscience of all citizens – from young children to the highest level of technical knowledge – within an appropriate cultural context. This paper documents the institutional roadmap, logic and progress in realizing that ambitious goal.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".