Conducting an Evaluation Framework for Disaster Management under Adaptive Organization Change in a School System

Abstract

This long-term study established a sustainable and resilient framework for enhancing organizational capacity and adaptability, based on adaptive thinking, for a school disaster prevention system (SDPS) for academic institutions located in a potential natural disaster area. Due to the movement of continental plates and the effects of tropical depressions, disasters occur frequently in Taiwan. We established a conceptual framework under aspects of organizational resilience for a SDPS for school institutions located in a potential disaster area under a choice experiment (CE) framework. We then evaluated the heterogeneity of staff perspectives on an adaptive disaster-mitigation program, as revealed by their preferences and estimated the marginal effects associated with various potential scenarios for such a program. We found that integrating stakeholder concerns about environmental issues, cooperating with local government drills, providing training to be disaster relief volunteers and cooperating with local government to implement disaster-prevention and protection projects were all valid program characteristics. This study also confirmed the existence of heterogeneity in the preferences of participants for adaptive management in SDPS context, as evidenced by their willing attitudes toward participation in education and training courses, participation in implementing disaster prevention and protection projects and undergoing training to be disaster relief volunteers. Specifically, the potential disaster prevention transformation program embodying these features was associated with the highest marginal willingness to work (MWTW). These outcomes can assist in the development and implementation of evaluation frameworks for organization-based management strategies in the context of SDPS.

Conducting an Evaluation Framework of Importance-Performance Analysis for Sustainable Forest Management in a Rural Area

“…The results are also consistent with the two binary choice model (Table 4). The GOF of our empirical model also meets the criterion of the LLR and AIC [88], which shows that the local people's participation behavior models of SFM had solid results with model specifications under the binary choice model and the normality of probit model, with adequate sample size [78][79][80]87,88].…”

“…The first model, had local people's participation awareness for SFM as the dependent variable while other factors on SFM were excluded, with the overall importance mean of SFM indicators as the independent variables. We performed the comparisons between logit and probit regression analysis, applying binary choice theory [87,88]. Finally, the model's goodness of fit (GOF) was evaluated by the Akaike information criterion (AIC) and log-likelihood ratio (LLR) [78][79][80]87,88].…”

“…We performed the comparisons between logit and probit regression analysis, applying binary choice theory [87,88]. Finally, the model's goodness of fit (GOF) was evaluated by the Akaike information criterion (AIC) and log-likelihood ratio (LLR) [78][79][80]87,88].…”

Constructing an adaptability evaluation framework for community disaster management from an earthquake event

“…The purpose is to deeply understand communities' their awareness and preference of disaster prevention to formulate a set of disaster management measures that are in line with the community resident's implementation. We adopt the choice experiment (CE), which can confirm the level to participants show heterogeneity in their preferences for adaptive management in the context of community disaster prevention [4,5,6], as reflected in whether they are willing to participate in disaster management courses, disaster prevention training, and disaster prevention drills and other related activities, as well as to receive training to become a member of the disaster prevention vanguard of the community. Specifically, the potential disaster management transformation plan that reflects these characteristics is related to the highest marginal willingness to participate (MWTP) [7,8].…”

Deep Learning for Safer School Infrastructure: An Interdisciplinary and Cross-organizational Collaboration

“…After testing different deep learning methods and algorithms using the Tensorflow Machine Learning library (Abadi et al, 2016;Krohn, 2018;Tensorflow.org, 2019) and Keras as a front-end to the library (Chollet & others, 2015;Gulli & Pal, 2017;Lamb, 2020), the final deep learning system utilized transfer learning and was based on the widely used InceptionResnetV2 system (Szegedy et al, 2017(Szegedy et al, , 2016. This hierarchical deep learning architecture predicted a given building's type, main structural system, and number of stories with accuracies of 81%, 67%, and 95%, respectively.…”