Governance Arrangements for Integrated Water Resources Management in Ontario, Canada, and Oregon, USA: Evolution and Lessons
Bibliographic record
Abstract
Guidelines produced by some major international organisations create a misleading impression that Integrated Water Resources Management (IWRM) can be implemented in a standardized fashion. However, contextual conditions vary from place to place, and differences in beliefs, attitudes, customs, and norms sensibly influence interpretation and implementation. Experiences with IWRM in Oregon (USA) and Ontario (Canada) are examined with regard to scope, scale, responsibility, engagement, finances and financing, and review processes and mechanisms. Development of IWRM and the evolution of governance have been shaped by different concerns and beliefs. Oregon has adopted a locally-driven and entrepreneurial approach, whereas Ontario developed a co-operative inter-governmental approach. In both cases, IWRM governance has also evolved due to changes in funding and priorities, which have benefitted some catchments and communities more than others. Both cases provide positive examples of reflexivity and resilience, and demonstrate the importance of review processes and strong cross-scale connections for effective governance. While underlying principles may be relevant for other locations, it would be a mistake to think that either of the two approaches for IWRM could be replicated elsewhere in their exact form. Implementation of IWRM in other parts of those countries and the world should, therefore, start with careful analysis of the local context, and existing governance arrangements and governmentalities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".