Participatory Water Management Strategies: Contributions for Canada from Brazil’s National Water Resources Policy
Bibliographic record
Abstract
Canadian decision-makers are encountering escalating socio-ecological pressures to introduce a national water strategy. Canada lags behind other countries such as Brazil which has had a comprehensive, participatory, watershed-based national strategy for over a decade. Similar to Canada, Brazil is a complex, federal, resource-based economy. These two states are world leaders in terms of possessing the vast quantities of the world’s fresh water supplies and in hydro-electric power production. In both cases, however, water abundance is predominantly concentrated in their northern territories with low population density, whereas in other geographical regions, the water demand associated with high population density lead to drought, shortages and social and economic inequalities. Despite these similarities, there are a number of differences particularly with respect to socio-economic and political structures. An examination ofthe Brazilian national water strategy offers some explanations as to why that federation has been able to develop innovative legislation as an important first step towards water security – a step that Canadahas yet to take. It also offers some very useful examples and lessons about how a federal state such as Canada might introduce and implement its own integrative national water strategy.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".