| ID | 181660 |
| Title Proper | Machine learning approach to the study of German strategic culture |
| Language | ENG |
| Author | Doeser, Fredrik ; Tappe, Jonathan |
| Summary / Abstract (Note) | This article introduces supervised machine learning to the study of German strategic culture, analyzing both how German strategic culture has changed and the impact of strategic culture on Germany's military engagement between 1990 and 2017. In contrast with previous qualitative research on strategic culture, supervised machine learning can yield measurable and empirical insights into strategic culture and its effects at any given point in time over a very long period, based on the reproduction of human coding of a very extensive set of security policy documents. The article shows that German strategic culture has changed slowly and in a nonlinear way after the Cold War, and that strategic culture, when controlling for confounding variables and the temporal order, has a measurable impact on Germany's military engagement. The article demonstrates the analytical value of machine learning for future studies of strategic culture. |
| `In' analytical Note | Contemporary Security Policy Vol. 42, No.4; Oct 2021: p.450-474 |
| Journal Source | Contemporary Security Policy Vol: 42 No 4 |
| Key Words | Germany ; Strategic Culture ; Military Engagement ; Supervised Machine Learning |