Integrating Resilience into Risk Matrices: A Practical Approach to Risk Assessment with Empirical Analysis
Bibliographic record
Abstract
The changing and intensifying landscape of global, national, and local disaster risks, driven by socio-political, environmental, and technological shifts, underscores the critical need for risk assessment by international agencies and governments. The Risk Matrix, introduced in 1995, has been widely used for risk assessment in different contexts, lauded for its simplicity and effectiveness. This model relies on the core risk components of consequence and likelihood, making it a favored tool for risk managers. To enhance the precision of risk assessment, various adaptations and extensions of the risk matrix have emerged; while some indirectly address resilience aspects, none explicitly integrate resilience into the matrix. This paper explores the risk matrix and its extensions, advocating for the inclusion of resilience in risk assessment. It introduces an empirical approach to quantify resilience, through a survey targeting small and medium-sized businesses in Southern Ontario, Canada. By developing two types of risk matrices—one with resilience considerations and one without—our work demonstrates how resilience alters risk prioritization, highlighting the importance of preparedness. This research underscores the pivotal role of resilience in risk assessment and urges its explicit integration into risk matrices to enhance accuracy and efficacy. Through practical examples and empirical data, the paper builds a compelling case for the central role of resilience in modern risk assessment practices.
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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 | 0.065 | 0.150 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".