Quantifying social capital creation in post-disaster recovery aid in Indonesia: methodological innovation by an AI-based language model.

Abstract

Smooth interaction with a disaster-affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post-disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post-disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)-based language model, which allows researchers to re-examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well-connected personnel and religiously-based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI-based language model, effectively strengthen text-based qualitative research.