Knowledge building and community learning for a more sustainable future
Bibliographic record
Abstract
and optimum utilization of water resources can be achieved.The research paper by Fakeha Parween and Ajai Singh presents a review and analysis of implementation of the NWP by the eastern states of India.Concluding that many of the major recommendations of the NWP are yet to be implemented, the authors provide a suite of practical suggestions for moving forward.In the next paper, George Kiambuthi Wainaina presents his research into the adoption challenges for drip irrigation technology in Kenya.Blending a desktop review and key informant interviews, the author systematically analyses the barriers to adoption of drip irrigation in the Kenyan context to identify potential pitfalls in both policy and practice.Finding a clear gap in the context-specific literature, George calls for more research on important multidisciplinary areas, and for actors to publish their stories of both successes and failures.The influence of dams and barrages on water quality and phytoplankton diversity in the upper Ganga basin is the focus of the contribution by D.S. Malik and co-authors.Noting the vital role that water quality plays in freshwater biodiversity, the authors show that human activities have negatively impacted on water quality and biodiversity in the upper Ganga basin and highlight the importance of monitoring and regulating such impacts through policy and practice.In the final research paper of this issue, Joan Abla Ketadzo, Nsalambi V. Nkongolo, and Mark McCarthy Akrofi examine the quality of groundwater that forms the main water source for five major slums in Accra, Ghana.Noting that overall water quality was poor, and did not meet WHO standards, the authors identify the causes of groundwater pollution and lay out clear recommendations for how the situation can be improved.In this issue, we also announce a new section-the Water Policy Lab.We have invited Hemant Ojha, Basant Maheshwari, and Basundhara Bhattarai to write a Guest Editorial and introduce the Water Policy Lab approach, which aims to initiating dialogue among those who are concerned with two key challenges: water insecurity, and the disconnect between water knowledge and its application in policy and practice.This new section will be a regular feature in World Water Policy.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".