The cascading disaster risk of water, energy and food systems
Bibliographic record
Abstract
This study presents a modified Institutional Analysis and Development framework for the purposes of analysing and developing policies to address cascading disasters in interconnected water, energy, and food (WEF) sectors. The aim of the framework is to inform how policymakers can synchronize and coordinate cross-sectoral and trans-governmental policies to manage cascading WEF disasters. To justify its applicability, we have tested the framework in a WEF related cascading disaster case that occurred in Iqaluit – the capital of Nunavut in Canada. Iqaluit is a city with limited access and heavy dependency on imported food and energy. On 2 October 2021, Iqaluit residents first began reporting contamination in their piped water sources. It was revealed that the pollution occurred from a fuel leak in a storage site located near a water supply facility. To cope with the disaster, the Nunavut and Federal governments undertook a series of responses that resulted in some unpredicted consequences. The study concludes that compartmentalized and sector-specific disaster planning, and preparedness slow down government agencies’ responses to a hazard event. It also reveals that uncertainties associated with cascading disasters can be best understood and thus responded to through discursive learning.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".