Importing Notions of Governance: Two Examples from the History of Canadian Water Policy
Bibliographic record
Abstract
As stress on water resources increases from growing human demands and a changing climate, recognition of the need to develop effective strategies for water governance is expanding. Consequently, it is timely to consider the legacy of effective instances of water policy innovation that have been highly influential in water resource management in Canada. We present two historical examples of policy transfer – that is, when policy employed in one jurisdiction is adapted for use in another. The first is the late nineteenth-century adoption of water allocation law in the North-West Territories that was a noteworthy departure from how water had been allocated in eastern Canada. The second is the twentieth-century introduction of conservation authorities in Ontario as regional watershed-based management entities. These examples illustrate how, in an era of expert-driven natural resources management, notions of governance were adapted from Australia and the United States. They also reveal how the biophysically-based policy context of water influences which policy transfer mechanisms are appropriate for lesson-learning. We conclude that the potential for policy transfer and lesson-learning to shorten the policy innovation timeline must be viewed as a critical response to urgent and evolving demands on water.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".