Integrated Water Resources Management: A Practical Solution to Address Complexity by Employing the Nexus Approach
Bibliographic record
Abstract
"Integrated Watershed Resource Management (IWRM), while a popular concept, is difficult to implement successfully. Many authors have expressed concern that few IWRM projects have resulted in improvement in the way we manage water and few have solved the emerging water quantity and quality problems. The two main criticisms are: 1. Restructuring the many government agencies responsible for water and making them work in an integrated or collaborative manner is an unrealistic proposition, and 2. The issues are too complex to be solved in an integrated manner, because there is a disconnect between the land, water, governance, social and environmental factors and human behaviour. Moreover, these activities are mostly unpredictable. Few of the critical authors offer a viable alternative, because neither the traditional top-down sectoral approach nor the integrated multi-stakeholder approach has been successful in solving all the water problems. It is proposed that the IWRM be practiced within watersheds, because it is the only sensible way to address the complex land, water, human and environmental issues. However, a nexus approach is required that makes linkages between the top-down and bottom-up approach and that includes selective stakeholders to develop and implement IWRM plans. It is argued that the multi-stakeholder process needs to be streamlined to a more problem solving session with fewer and selective participants. This will speed up the process and allow more resources to be assigned to management actions. This also requires a commitment by the researchers and managers to monitoring the effectiveness of the implemented management over time. It is also advocated that the IWRM approach can only be successfully implemented at the microand meso-scale and not at the river basin scale. Finally, another objective of this paper is to shed light on the issue of scale and its role in influencing the effect of spatial and temporal factors on environmental and social outcomes of integrated management of water resources. Can one conclude for instance that IWRM approaches can only be successfully implemented at the micro- and meso-scale and not at the river basin scale?"
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".