South Asian Water Leadership Programme on Climate Change
Bibliographic record
Abstract
The South Asia Consortium for Interdisciplinary Water Resources Studies (Saci- WATERs) a water policy research institute based in Hyderabad, India, launched the South Asian Water (SAWA) Leadership Programme on climate change in 2017. The SaciWATERs is hosting the programme in collaboration with four partner engineering institutes from four South Asian countries, and with funding support from the International Development Research Centre (IDRC), Canada. This academic-oriented programme is aimed at facilitating the creation of a group of interdisciplinary women leaders in South Asia that share a common understanding of the crosscutting scientific and societal issues of water resource management. The four-year (2017-2021) SAWA leadership programme has granted fellowships to 36 fellows that were selected from the partner institutes namely Bangladesh University of Engineering and Technology, Dhaka; Nepal Engineering College, Kathmandu; University of Peradeniya, Kandy; and Anna University, Chennai. The programme places emphasis on intensive training in the application of research methods that include gender and social approaches and in leadership skills development through activities such as team-building sessions, application of negotiations and conflict resolution in the field, mentorship and networking. The project also collaborates with governments, NGOs and the private sector to facilitate internships in order to provide an authentic work environment allowing candidates to link their research to actual decisions and applications within the communities with which they are engaging. In addition, it promotes a common understanding of the way social and cultural interpretations of gender intersect with the issues of climate change and water insecurities. It does so not only among the male and female students enrolled in an IWRM master’s programme but also among faculty members, through trainings and common curriculum development across the four engineering institutions. This allows for the development of a broad base of trainers and researchers, both men and women, who will share the leadership programme’s vision.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.167 |
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; both teacher heads agree on what is shown here.
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".