The cascading disaster risk of water, energy and food systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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