The comprehensible style of legal texts seems to be a predominantly linguistic problem. This is how the plain-legal-language movements present it. But, while plain-language statutes have been on the agenda for decades in every civilised country, laws still become more and more complicated. The paper attempts to explain this controversy. First, it argues that comprehensibility has more aspects beyond the linguistic or stylistic one. Sometimes it is the linguistically simplest texts that raise the most serious comprehensibility problems. The paper refers to two pieces of corpus linguistic research that provide evidence that vocabulary and grammar in themselves do not explain the incomprehensibility of the legal texts. Second, for a more subtle handling of the comprehensibility problem, the paper offers a framework of three typical pragmatic situations – the processual, the problem-solving and the compliance settings – where comprehensibility problems arise in different ways. The conclusion of the paper is that, contrary to the usual explanation that the main reason for incomprehensibility is that, in law, clarity and accuracy can be only employed at each other's expense, it is rather the systemic and interpretive character of law and the growing importance of technical rules that hinder the understanding of legal texts.

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The connection between Big Data (BD) and law can be thematised in several ways. This article aims to contribute to the understanding of the different levels of interplay between Big Data, law and legal science. The paper firstly considers Big Data as the subject of legal regulation. Accordingly, it overviews the moral questions surrounding Big Data, BD's predictive potential as well as the impacts of it on legal framework rules regarding privacy, data protection, competition and business regulation. The next section understands Big Data as a tool in the regulator's and the lawyer's hand. It discusses the new ways of 'Big Data-based social engineering' as well as the creation of predictive tools and inferencing techniques based on Big Data in policing, law enforcement and litigation. Then the paper investigates the use of BD in legal science, thus the fourth section considers Big Data as a research tool. It seeks to explore the use of legal data-sets and textual corpuses as BD. In addition, it sheds some light on the wider impacts of statistical analysis, natural language processing, content analysis, machine learning and behavioural prediction on legal science. Finally, the paper gives some insight into the relationship between traditional doctrinal scholarship and the new types of BD-based research.