Big Data and the Human and Social Sciences

The emergence of big data and data science has caused the human and social sciences to reconsider their aims, theories, and methods. New forms of inquiry into culture have arisen, reshaping quantitative methodologies, the ties between theory and empirical work. The starting point for this article is two influential approaches which have gained a strong following, using computational engineering for the study of cultural phenomena on a large scale: ‘distant reading’ and ‘cultural analytics’. The aim is to show the possibilities and limitations of these approaches in the pursuit of scientific knowledge. The article also focuses on statistics of culture, where integration of big data is challenging procedures. The article concludes that analyses of extensive corpora based on computing may offer significant clues and reveal trends in research on culture. It argues that the human and social sciences, in joining up with computational engineering, need to continue to exercise their ability to perceive societal issues, contextualize objects of study, and discuss the symbolic meanings of extensive worlds of artefacts and discourses. In this way, they may help to overcome the perceived restrictions of large-scale analysis such as the limited attention given to individual actors and the meanings of their actions. Full article

We are living in a data-driven society. Big Data and the Internet of Things are popular terms. Governments, universities and the private sector make great investments in collecting and storing data and also extracting new knowledge from these data banks. Technological enthusiasm runs throughout political discourses. “Algorithmic regulation” is defined as a form of data-driven governance. Big Data shall offer brand new opportunities in scientific research. At the same time, political criticism of data storage grows because of a lack of privacy protection and the centralization of data in the hands of governments and corporations. Calls for data-driven dynamic regulation have existed in the past. In Chile, cybernetic development led to the creation of Cybersyn, a computer system that was created to manage the socialist economy under the Allende government 1971–1973. My contribution will present this Cybersyn project created by Stafford Beer. Beer proposed the creation of a “liberty machine” in which expert knowledge would be grounded in data-guided policy. The paper will focus on the human–technological complex in society. The first section of the paper will discuss whether the political and social environment can completely change the attempts of algorithmic regulation. I will deal specifically with the development of technological knowledge in Chile, a postcolonial state, and the relationship between citizens and data storage in a socialist state. In a second section, I will examine the question of which measures can lessen the danger of data storage regarding privacy in a democratic society. Lastly, I will discuss how much data-driven governance is required for democracy and political participation. I will present a second case study: digital participatory budgeting (DPB) in Brazil. Full article

This paper focuses on two basic archival sources of Ottoman urban history: avârız tax surveys and surety surveys of Edirne in the second half of the seventeenth century. These tax and surety surveys, which are abundant in the Ottoman archives, contain rich information on the residents of Ottoman cities, including names, titles, occupation, gender, religion, property status and numbers of tax units (avârızhane). All this information is given on the basis of the mahalle (neighborhood), which provides a practical point of departure for “reading” the Ottoman city. Each register contains approximately 10,000 household heads, with about 10 different attributes listed for each of them. In addition, the data contain some numerical information (e.g., numbers of tax units), although a majority of it is nominal. While some types of nominal data, like religion and gender, comprise only a few possible variants and can thus be analyzed without further classification, others, such as occupation data, contain hundreds, and thus cannot be analyzed without clustering them. In analyzing the data, the distribution of each attribute in the city and its density in an urban space (scaling) can be presented in the form of ratios. One may perform these analyses in the first stage with conventional statistical methods. However, this study attempts for the first time to achieve two further goals: connecting the data types to each other; and highlighting the distinguishing differences among the mahalles using the methods of hierarchical clustering, correspondence analysis, and creating maps by geographic information systems (GIS) applications—none of which is possible with conventional methods. Such an exploration suits both the relational approach I am trying to advocate here—namely, that all elements in the city must be understood in relation to one another—and my effort to lay out the general features of the Ottoman city. This approach will allow us to see how these attributes are spatially distributed based solely on the guidance provided by the big data available in the sources. In this context, I explore the topographical similarities of the mahalles on the one hand, and the socio-economic features and structures of attribute profiles via the scale of their “corresponding distances” on the other. These topographical vicinities and socio-spatial neighbors resemble and do not resemble each other in the city. This paper discusses the processes, challenges and possible contributions of the application of big data to urban historical studies. Full article