Implementing Integrated Water Resources Management: The Importance of Cross-Scale Considerations and Local Conditions in Ontario and Nova Scotia
Bibliographic record
Abstract
Integrated water resources management (IWRM) is advocated by international and expert communities as the most viable approach to achieving sustainable freshwater management. Watersheds are often viewed as the preferred management units. There is increasing recognition, however, that socio-political and watershed boundaries do not coincide, and where they are used for management purposes, these boundaries are constructed through processes of political contestation. Key informants from various agencies and sectors associated with water resources management in Ontario (where watershed-based management has been in place for decades), Nova Scotia (currently developing a comprehensive water resources management strategy), and the Government of Canada were interviewed: to explore the links between IWRM and watershed management; barriers to IWRM; elements essential for IWRM to work effectively; the appropriate scale of watershed management units; and the degree of cross-scale interactions between agencies and stakeholders. Four main themes emerged around capacity, coordination and participation, scale of implementation, and education. To achieve IWRM, particular attention must be paid to existing local circumstances and resources, situated within formalized provincial and national frameworks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".