Collaborative governance and integrated risk management framework of natural disasters
Bibliographic record
Abstract
Purpose This study aims to align the objectives of key stakeholders by developing an integrated framework for high-impact natural disaster risk management. High-impact natural disasters have emerged as one of the most challenging policy issues. Design/methodology/approach The authors have applied the thematic text analysis for analysing the list of questions essential to develop an integrated framework. For theory, the authors have used the theoretical framework of collaborative governance. Findings The current work explains how to identify key stakeholders. Furthermore, it describes the framework to fit stakeholders' actions into the actionable components of risk management. Additionally, this framework also helps the firms that fall under the category of the industries “in the proximity of risks” and “the support industries” in modifying their role in the context of natural disaster risk. Research limitations/implications The limitation of this framework is that the authors relied on commonly occurring natural disaster risks to develop the framework. Therefore, risk-specific aspects are less likely to be thoroughly covered in this framework. However, this limitation is not directly impacting the goal of this study. Additionally, in the future more comprehensive framework with the additional element in the existing framework can overcome these limitations. Practical implications The findings of the study offer insights that can be useful for policymakers in developing various preventive strategies. Managers can use the results and align their objectives with policy goals. Social implications Socially, if communities try to design local risk management strategies, this framework would be helpful. Originality/value This study illustrates the role of objective alignment in high-impact risk management. This study is crucial in extending current knowledge on high-impact natural disaster risk management.
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 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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".