Carolina Ferrer, Université du Québec à Montréal (UQAM), Canada
The purpose of this research is, firstly, to map the 48 national literatures of Europe, through the exploration and the analysis of the bibliographic references contained in the main literary database, the Modern Language Association International Bibliography. Secondly, the series obtained are correlated to economic and development indicators in order to determine whether and how the cultural, economic, and social fields interact with each other. From the theoretical viewpoint, this project stands at the crossroad of several concepts: the literary field defined by Pierre Bourdieu (1972, 1980, 1992), knowledge domain analysis (Hjørland& Albrechtsen 1995; Hjørland 2001; Nascimento & Marteleto 2008), scientometrics (Price 1963; Garfield 1980, 2005; Leydesdorff 1998), and the recently emerged concept of big data (Berman 2013; Boyd & Crawford 2012; Mayer-Schönberger& Cukier 2013). Methodologically, aiming at quantitatively identifying the European national literatures, we base our research on scientometrics. Initially developed by Price (1963), the purpose of scientometrics is to measure and to analyze the scientific and technological activity. In this study, we adapt scientometric indicators to the architecture and features of the Modern Language Association International Bibliography. Thus, the elaboration of bibliometric indicators (Garfield 1980, 2005; Hjorland & Albrechtsen 1995) allowed us to obtain the number of bibliographic references dedicated to the study of each of the 48 European national literatures, making it possible for us tovisualize the importance of each of these literatures and to compare them to economic and social indicators.
[Keywords: European literary field, bibliographic databases, data mining, big data, digital humanities, quantitative methods, economic indicators, social indicators]
Digital humanities and big data
In «A genealogy of digital humanities», MarijaDalbello (2011) proposesa definition of digital humanities:
the ability to read the archive of core texts, together with their residual materiality from previous media contexts in order to produce intensive modes of engagement with particular documents, groups of texts, and the archive is brought to broader audiences. (Dalbello, 2011, p. 497)
The following year, Boyd and Crawford (2012), define Big Data «as a cultural, technological, and scholarly phenomenon that rests on the interplay of technology […], analysis […], mythology» (Boyd & Crawford, 2012, p. 663). The technological aspect corresponds to the capacity of extracting, storing, and putting in relation immense sets of information. Analytically, these massive amounts of information make it possible to identify patterns that allow us to obtain economic, social, and technical conclusions about the behaviour of the series. The authors consider that the belief that big datasets represent superior knowledge capable of yielding truthful, objective, and exact results is only a mythology.
In 2013, Cukier and Mayer-Schoenberger (2013) establish that the phenomenon of massive information implies a change in the way we consider data. Firstly, we can no longer consider a sample of data, since huge amounts of data are available. However, this considerable amount of information implies a certain uncleanness of information. Thus, this change means, secondly, that we have to accept the existence of some inexact data, an amount that is meaningless given the quantity of information available. Finally, frequently, this data does not allow us to know the causes of the phenomena considered, allowing us only to correlate the series. Thus, there is a displacement from the determination of the causes of the events observed to their descriptions: instead of explaining the past, the correlations are used to predict the future. Moreover, as Berman (2013) points out: «Big Data provides quantitative methods to describe relationships, but these descriptions must be transformed into experimentally verified explanations» (p. 226). In this analysis, we will base our explanation on Bourdieu’s study of the behaviour of the literary field (1992).
Once the definitions of digital humanities and big data established, we should examine if there is a relation between these concepts. Thus, we have extracted from the ISI Web of Knowledge the references that correspond to these concepts…Access Full Text of the Article