Systems Approach to Management of Disasters – A Missed Opportunity
Bibliographic record
Abstract
Everyday life is overwhelmed by critical phenomena that occur on specific spatial and temporal scales. Typical examples are floods, landslides, storm surges, and similar. All these phenomena might have, whenever they occur, significant negative consequences for human lives. They often result from complex dynamics involving interaction of innumerable system parts within three major systems: (i) the physical environment; (ii) the social and demographic characteristics of the communities that experience them; and (iii) the buildings, roads, bridges, and other components of the constructed environment. In non-scientific terms, such events are commonly referred to as disasters. Proper management in the face of a natural disaster necessitates a transformation of attitude towards integration of economic, social and environmental concerns related to disasters, and of the actions necessary to deal with them. Recent trends in confronting disasters include consideration of the entire region under threat, explicit consideration of all costs and benefits, elaboration of a large number of alternatives to reduce the damages, and the greater participation of all stakeholders in decision-making. Systems approaches based on simulation, optimization, and multi-objective analyses have great potential for providing appropriate support for effective disaster management in this emerging context. The systems approach to managing disasters outlines proven strategies for pooling interdisciplinary resources more efficiently to boost emergency responses. Looking at the disaster management practice, with primary focus on Canada, this paper explores the question of why advances in systems theory have failed on a broader scale to majorly transform management of disasters. The paper identifies whether and how that knowledge and systems science can be deployed to improve disaster management in the face of rapid climate destabilization so that sustainability becomes the norm, not the occasional success story.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".