COLLABORATIVE DECISION-MAKING FOR DROUGHT MANAGEMENT: IMPROVING MULTI-ACTOR APPROACHES
Bibliographic record
Abstract
Drought management can be highly challenging; droughts can be experienced over a large geographic area, and the extent and severity of impacts can be exacerbated by local water uses.1 In Ontario, these uses might include agriculture, aggregate washing, and watering at golf courses. Oftentimes, droughts are part of normal ecological cycles, but the risk and hardship faced by water-based industries and the public make drought a particularly important policy challenge. Technical approaches to managing drought promote the use of monitoring standards, early warning systems, and planned management actions. Building social capital and strengthening relationships can also contribute to reducing vulnerability through building adaptive capacity and reducing exposure and sensitivity.2 Collaborative approaches, created by government to generate policy and program recommendations for drought management, can provide a local view on drought challenges and a balanced viewpoint that includes all voices affected by decisions. An example of this type of collaborative relationship is Ontario Low Water Response and Water Response Teams. Ontario Low Water Response convenes collaborative groups – known as Water Response Teams – to determine the severity of drought in local watersheds and provide recommendations to the provincial government, including recommendations to declare a drought ‘emergency’, which triggers water restrictions in affected areas. One key challenge of this process is that Water Response Teams have recommended declaring water restrictions during severe low water conditions. However, the province has never enforced restrictions. Governments not following the recommendations of collaborative groups they have created to comment on policy problems is a common finding in collaborative governance research. The key focus of this research is to understand the role of Water Response Teams in decision-making, and to explore how international experiences can inform the Ontario drought management process.
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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 | 0.052 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".