Foreword: Understanding Constitutional Reasoning

Abstract

Constitutional reasoning can be defined in many different ways, thus potentially delineating many distinct research agendas. First, consider the term reasoning. Reasoning can refer to the justifications an agent or someone on her behalf publicly adduces for choosing a certain course of action, such as adopting a certain policy or solving a dispute in a certain way. Alternatively, though, it can refer to the motives and mental processes that drove or caused the agent to choose the course of action under discussion. It may be said that, in the first case, reasoning refers to the agent's justificatory reasons, whereas, in the second, it refers to her motivating reasons. 1 Related distinctions are those contrasting justifications and reasons for action, 2 justificatory and explanatory reasons, 3 or context of justification and context of discovery. 4 Justificatory and motivating reasons are independent from one another. Provided the agent's motives are noble enough, the agent may publicly offer them as justification for her course of action, but it need not always be so. Often, the agent will refrain from revealing her true motives and will instead put forth reasons that the agent believes others are more likely to regard as valid and legitimate. The distinction applies for public decision-makers-legislators, judges, bureaucrats-as well as for ordinary human beings in the most ordinary situations of everyday life-from courting a potential spouse, to reviewing a colleague's work. We do not always say what

Matter of Opinion: Assessing the Role of Individual Judicial Opinions at the International Criminal Court

“…Clustering 'aims to identify coherent, but a priori unknown document groupings' . 45 The main text-mining method used is known as probabilistic topic modelling.46 Topic models are used as an unsupervised document-clustering technique to generate thematic annotations automatically and are common in machine learning and natural (legal) language processing. Topics are modelled as probability over words and documents as probability over topics.…”