Knowledge in Intersectoral Research
2024-11-20
Problem
Intersectoral research combining Artificial Intelligence (AI), social sciences, and urban planning is crucial for tackling complex urban challenges like climate change and public health. However, ensuring the validity of knowledge across these diverse fields is difficult due to different epistemic and ontological frameworks. Traditional metrics often fall short in capturing the interdisciplinary nature, leading to lower funding success and challenges in maintaining credibility and relevance.
Method
We reviewed the most cited papers from 2014 to 2024 across AI, social sciences, and urban planning. The analysis focused on six dimensions:
- Ontological
- Epistemological
- Methodological
- Teleological
- Axiological
- Valorization
Based on this, we developed a validation framework to guide researchers in selecting appropriate methodologies and epistemologies. The framework was tested through case studies and refined with input from experts in each field.
Findings
Our framework categorizes perspectives within the six dimensions, promoting a structured approach to validate intersectoral studies:
- Ontological: Defining reality in city science and AI.
- Epistemological: Understanding knowledge scope in interdisciplinary research.
- Methodological: Choosing methods that integrate AI, social sciences, and urban planning.
- Teleological: Aligning research with societal needs.
- Axiological: Incorporating values and ethics.
- Valorization: Ensuring practical impact.
Interactive Visualization
Explore the detailed concepts here.
Impact
This framework bridges disciplinary silos, enhancing the credibility and relevance of intersectoral research in city science. It provides a clear guide for researchers to adopt or avoid certain perspectives, ensuring socially accountable knowledge. Future steps include testing the framework in other domains, expanding stakeholder engagement, integrating with policy development, and developing educational programs.