Examining water risk perception and evaluation in the corporate and financial sector: a mixed methods study in Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract As primary users of a socially, economically, and environmentally significant yet increasingly stressed resource like water, the corporate and financial sectors have an important role in sustainable water management. However, extant literature reveals a gap in the empirical assessment of water risk perception and its influence on water risk evaluation and decision-making in the corporate and financial sectors. Our explanatory sequential mixed methods study examined the relationship between water risk perception and risk evaluation (risk ratings), addressing these gaps. We employed a cross-sectional survey (N = 25) followed by semi-structured interviews (N = 22), with a purposive expert sample of analysts, practitioners, and decision-makers in the corporate and financial sector in Ontario, Canada. Our study finds multi-dimensional risk perception factors, including knowledge, professional experience, perceived controllability, values, trust, location, and gender, that influence water risk ratings and vary with the type of risk. Moreover, the in-depth follow-up interviews reveal multiple drivers of different risk ratings, such as proximity bias, sector differences, trust in various institutions, as well as the influence of tacit knowledge, exposure, the role of regulations, media, and financial materiality. Our study empirically concludes that the water risk perception of analysts, practitioners, and decision-makers in the corporate and financial sectors is highly nuanced and impacts the evaluation of different water risks, and should be systematically integrated into risk assessment and decision-making frameworks. Our study advances knowledge in the fields of risk analysis and sustainable water management and contributes by empirically examining and explaining the complex and underexplored relationship between water risk perception factors and evaluation using novel interdisciplinary Risk Theory and mixed methods approaches. Finally, the study’s findings can help integrate sector and location-specific preferences and priorities with analytical data to design contextually-attuned decision support tools for sustainable water management strategies, policies, and practices.
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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 | 0.009 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it