The importance of processes of social learning for transboundary water management - Perspectives from the HaminiCOP project
Bibliographic record
Abstract
Integrated management of transboundary river basins poses major challenges to the integration of different administrative, legal, cultural, institutional and economic traditions. The development of a management plan requires that actors involved communicate and cooperate. Integrated models and DSS may be very useful in informing the development and implementation of management plans and new resource management regimes. However, their success depends on the process in which they are embedded. The current paper advocates the importance of stakeholder and public participation for the successful development and implementation of river basin management plans. \nThe HarmoniCOP (Harmonizing COllaborative Planning) started October 2001. Its main objective is to increase the understanding of participatory river basin management in Europe. It aims to generate practically useful information about and improve the scientific base of social learning and the role of IC-tools in river basin management and support the implementation of the public participation provisions of the Water Framework Directive. Social learning is involves learning processes of the different groups about their biophysical environment and about the social interactions. Collective action and the resolution of conflicts require that people recognize their differences and learn to deal with them constructively.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".