Developing a transdisciplinary tool for water risk management and decision-support in Ontario, Canada
Bibliographic record
Abstract
Abstract Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".