From narrative to machine-readable logic: Formalising and validating local indigenous knowledge for rural early...

Area of Science:

• Disaster Management
• Community-Based Adaptation
• Indigenous Knowledge Systems

Background:

• Technological early warning systems (EWS) in rural Indonesia face limited adoption due to a disconnect with community-trusted contextual triggers.
• Information and Communication Technology (ICT)-based systems show underutilization and limited perceived benefits, despite the critical role of Local Indigenous Knowledge (LIK) in disaster preparedness.
• Existing frameworks for integrating LIK into disaster technologies often rely on expert-driven, unvalidated rules, hindering credibility and scalability.

Purpose of the Study:

• To propose and validate a socio-technical integration framework for systematically incorporating Local Indigenous Knowledge (LIK) into technological early warning systems (EWS).
• To address the limitations of current expert-driven approaches by developing a framework that reduces dependence on tacit expert judgment.

Main Methods:

• A qualitative inquiry involving 17 in-depth interviews with fishermen and a focus group discussion (FGD) with 8 community leaders from three Indonesian coastal provinces.
• Validation of the framework's contextual foundation through a survey of 438 fishermen in the same regions.
• The proposed framework includes three stages: LIK acquisition from community experience, validation through empirical community consensus and scientific explanation, and structured integration into EWS.

Main Results:

• The study developed a novel socio-technical integration framework for incorporating LIK into EWS.
• The framework's three-stage approach (acquisition, validation, integration) was validated through community engagement and empirical data.
• This staged validation reduces reliance on expert interpretation and supports data-driven decision-making.

Conclusions:

• The proposed socio-technical framework offers a scalable and credible method for integrating Local Indigenous Knowledge into early warning systems.
• This approach enhances the effectiveness of EWS by aligning technological systems with community-specific disaster triggers and validation processes.
• The findings support the development of more robust and trusted disaster preparedness strategies in rural coastal communities.