Structure and bias in the network autocorrelation model

In a recent paper (Mizruchi and Neuman, 2008), we showed that estimates of ρ in the network autocorrelation model exhibited a systematic negative bias and that the magnitude of this bias increased monotonically with increases in network density. We showed that this bias held regardless of the size of the network, the number of exogenous variables in the model, and whether the matrix W was normalized or in raw form. The networks in our simulations were random, however, which raises the question of the extent to which the negative bias holds in various structured networks. In this paper, we reproduce the simulations from our earlier paper on a series of networks drawn to represent well-known structures, including star, caveman, and small-world structures. Results from these simulations indicate that the pattern of negative bias in ρ continues to hold in all of these structures and that the negative bias continues to increase at increasing levels of density. Interestingly, the negative bias in ρ is especially pronounced at extremely low-density levels in the star network. We conclude by discussing the implications of these findings.     

Configuration to conviction: Network structures of political judiciary in the Austrian Corporate State

In this paper, we analyze the extent of political judiciary in the transformed system of the Corporate State of Austria using the computational methods of a network approach. We investigate the differences in the legal prosecution of the political opposition, namely of members of the Communist, Social Democratic and National Socialist parties based on Vienna as a case study. Based on over 1,800 court records from 1935 processed at the Viennese provincial courts, we evaluate the courts’ practice in contrast to the official legislature during the consolidated phase of the regime. In this study, we examine whether the law was strategically utilized against specific groups (following the concept of Kirchheimer (1965)’s political lawsuit), and as in the more lenient version of Fraenkel (1927/1968)’s tendency justice, we analyze whether the law was disadvantageously interpreted for political partisanship up to a blatant breach of conduct. Combining quantitative and qualitative methods with network science approaches, we identify patterns of political prosecution and structural predispositions for the sentencing of left- and right-wing groups of the political opposition. We can prove different practices of political judiciary and differentiate between the different treatment of Social Democrats, Communists and National Socialists in 1935 in Vienna. We identify specialized strategies to prosecute the political opposition, resulting in a clear bias against left-wing groups and a relative leniency in the conviction of National Socialists based on the evolution of charges in the courts’ actions. Using a multimodal network approach, we reveal key players and cooperation of judges and prosecutors which accounted for harsher sentences. We provide evidence that the system of control over the judiciary and over the political opposition was already crumbling in the Austrian capital in 1935, even before the “Anschluss” to NS-Germany in 1938.     

The use of different data sources in the analysis of co-authorship networks and scientific performance

Scientific collaboration is usually derived from archival co-authorship data. Several data sources may be examined, but they all have advantages and disadvantages, especially when a specific discipline or community is of interest. The aim of this paper is to explore the effect of the use of three data sources – Web of Science, Current Index to Statistics and nationally funded research projects – on the analysis of co-authorship networks among Italian academic statisticians. Results provide evidence of our hypotheses on distinct collaboration patterns among statisticians, as well as distinct effects of scientist network positions on scientific performance, by both Statistics subfield and data source.     

The evaluation of workers by customers as a method of control and monitoring in firms: Digital reputation and the European Union's General Data Protection Regulation

As a method of surveillance and monitoring, the evaluation of workers by customers and employers and the disclosure of the results pose a series of challenges for the current legal framework of the European Union (EU). Employees subject to such evaluations are exposed to a far more intense and wider degree of monitoring of their work than traditional workers. The phenomenon arises from the adoption of a customer perspective, seeking to make work observable at all times, without any cost to firms. In this light, the author analyses the EU's General Data Protection Regulation, which establishes very specific restrictions when requesting and disclosing information about workers.