Risk Management Strategies and the Role of Social Context: A Comparative Study
Bibliographic record
Abstract
Researchers have been suggesting that there is a need to examine the wider social context and its role in influencing flood risk management strategies; this has also been joined by a call for further research into the risks of increased rainfall as part of overall climate change. In response to these calls this research study examines the case studies of two pluvial, meaning of or caused by rainfall, flood events; the Calgary, Canada floods of 2013 and the Montrose, Scotland floods of 2016. These events were considered to be 1 in 100 year low probability scenarios and caused significant disruption to the affected areas. The study focuses on the examination into the social context of such events; by examining the risk perceptions before the event, the flood management strategies used and the social impact of the events it was possible to gain insight into the wider picture of pluvial flooding. The analysis of the cases demonstrated that the perceptions of the events were low, due to the unusual nature of the events, but the forecasting of a flood and the issuing of warnings helped to reduce the impact and predict the areas that were most likely to be affected. It has also highlighted the importance of setting common goals, and engaging with, all the necessary stakeholders to improve the effectiveness of strategies and responses. The study concludes by indicating issues that may be of interest to decision makers and researchers in the field of risk management.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".