Towards adaptive and integrated management paradigms to meet the challenges of water governance
Bibliographic record
Abstract
Integrated Water Resource Management (IWRM) aims at finding practical and sustainable solutions to water resource issues. Research and practice have shown that innovative methods and tools are not sufficient to implement IWRM - the concept needs to also be integrated in prevailing management paradigms and institutions. Water governance science addresses this human dimension by focusing on the analysis of regulatory processes that influence the behavior of actors in water management systems. This paper proposes a new methodology for the integrated analysis of water resources management and governance systems in order to elicit and analyze case-specific management paradigms. It builds on the Management and Transition Framework (MTF) that allows for the examination of structures and processes underlying water management and governance. The new methodology presented in this paper combines participatory modeling and analysis of the governance system by using the MTF to investigate case-specific management paradigms. The linking of participatory modeling and research on complex management and governance systems allows for the transfer of knowledge between scientific, policy, engineering and local communities. In this way, the proposed methodology facilitates assessment and implementation of transformation processes towards IWRM that require also the adoption of adaptive management principles. A case study on flood management in the Tisza River Basin in Hungary is provided to illustrate the application of the proposed methodology.
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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 | 0.024 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".