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Record W4403238871 · doi:10.1108/jabs-10-2022-0342

Collaborative governance and integrated risk management framework of natural disasters

2024· article· en· W4403238871 on OpenAlex
Ashu Tiwari

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Asia Business Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity Canada West
Fundersnot available
KeywordsCorporate governanceNatural disasterBusinessRisk managementCollaborative governanceRisk governanceEnvironmental resource managementEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.308
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it