Studying knowledge: an analytical guide for international politics

Alejandro, A.ORCID logo (2025). Studying knowledge: an analytical guide for international politics. In Bliesemann de Guevara, B., Kaczmarska, K., Kurowska, X., Poopuu, B. & Warnecke, A. (Eds.), Knowledge and Expertise in International Politics: A Handbook (pp. 126 - 140). Oxford University Press. https://doi.org/10.1093/oso/9780192871145.003.0010
Copy

Abstract

This chapter provides analytical guidelines and a critical handrail to help researchers design and self-assess research focusing on knowledge. To do so, it introduces a three-stage process to guide researchers studying knowledge through the analytical options available to them, which it illustrates based on a literature review focusing on knowledge in international politics. First, it introduces concepts commonly used to study knowledge as an exercise of conceptual-ontological clarification which requires naming and defining one’s focus by deciding on a concept that best captures one’s interest (e.g., data, skills, expertise, consensual knowledge). Second, the chapter provides a roadmap for research design by formalizing six dimensions commonly encountered and presenting options available for researchers in each of them: a) types of knowledge, b) types of agents, c) knowledge-related processes/roles/functions, d) contexts, e) observable manifestations of knowledge, f) other dimensions such as a focus on the material and structural. Third, the chapter invites readers to pay attention to three methodological problems commonly encountered in the study of knowledge: lack of contextualization, inappropriate generalization, and inadequate inference. Overall, this pedagogical guide is an exercise of analytical abstraction that facilitates the research process by making explicit analytical strategies that are often left implicit, thus accompanying researchers into producing more rigorous and coherent work.

Full text not available from this repository.

EndNote BibTeX Reference Manager (RIS) Refer Atom Dublin Core JSON Multiline CSV
Export