The Construction of the Concept of Peace in the Discourse of Nobel Peace Prize Laureates.

Epistemic authorities play a key role in co-construction processes that foster the consolidation or contestation of shared meanings, especially when facing controversial and multifaceted issues. This is the case of peace, a seemingly clear issue which is yet instrumentally evoked and used in function of even opposing meanings and practices. Even within peace studies, the debate about what peace is -and consequently about the scope of this scientific domain- has led to more restrictive views (i.e. peace as the opposite of war) and more proactive perspectives (such as those stressing the need for positive approaches, which should foster structural, cultural and direct peace). In this contribution we present a preliminary study which aimed to investigate whether it is possible to identify with semi-automatic approaches, diverse types of peace within institutional discourses issued by epistemic authorities. We draw on 1) Bobbio’s classification of pacifism, proposed in 1975, which is situated within a narrow view of peace studies. According to this model, it is possible to distinguish between Instrumental; Institutional and Finalistic approaches to pacifism; and 2) Anderson’s model of peace, proposed in 2004, which suggest to classify peace according to the macro and micro contexts to which it is directed. The speeches of Nobel Peace Prize laureates from 1960 to 2022 were manually analyzed according to the six categories corresponding to peace types (Bobbio, 1975) and the three levels of context (Anderson, 2004). Moreover, at an exploratory level, this study used topic detection models to observe which types of peace and contexts emerged, their relationship with thematic contents of the speech, their variation over time, and their relationship with individual characteristics of the Nobel laureate (e.g., gender). Finally, the study tested whether the classification system applied allowed for the automatic identification of the categories found. This last step was tested through different machine learning algorithms for classification (e.g., Support-Vector Machines, Random Forest). By shedding light on these issues, this study aims to enrich our understanding of the complex and dynamic nature of peace as a social construct and provide insights on the diverse approaches to peace and peacebuilding.