Akos Rona-Tas

Entrepreneurship and Self-Employment in Transition Economies

We argue that claims of an entrepreneurial miracle as a description of private sector development in post-communist Europe conflates entrepreneurship with self-employment. The difference between the two hinges on the Weberian distinction between enterprise- and household-centered businesses. We then present two paradigms, the entrepreneurial that emphasizes the first and the post-Fordist that stresses the importance of the second business type, and provide data on businesses and individual motivation of business owners. We find more support for the post-Fordist approach. Then we show that business forms, primarily associated with self-employment have different recruitment patterns and rewards than other, more entrepreneurial forms. We end with a plea to disaggregate the various forms of independent, private sector activity in future research.

Dissemination of Health Information within Social Networks

In this paper, we investigate, how information about a common food born health hazard, known as Campylobacter, spreads once it was delivered to a random sample of individuals in France. The central question addressed here is how individual characteristics and the various aspects of social network influence the spread of information. A key claim of our paper is that information diffusion processes occur in a patterned network of social ties of heterogeneous actors. Our percolation models show that the characteristics of the recipients of the information matter as much if not more than the characteristics of the sender of the information in deciding whether the information will be transmitted through a particular tie. We also found that at least for this particular advisory, it is not the perceived need of the recipients for the information that matters but their general interest in the topic.

Enlisting Supervised Machine Learning in Mapping Scientific Uncertainty Expressed in Food Risk Analysis

Recently, both sociology of science and policy research have shown increased interest in scientific uncertainty. To contribute to these debates and create an empirical measure of scientific uncertainty, we inductively devised two systems of classification or ontologies to describe scientific uncertainty in a large corpus of food safety risk assessments with the help of machine learning (ML). We ask three questions: (1) Can we use ML to assist with coding complex documents such as food safety risk assessments on a difficult topic like scientific uncertainty? (2) Can we assess using ML the quality of the ontologies we devised? (3) And, finally, does the quality of our ontologies depend on social factors? We found that ML can do surprisingly well in its simplest form identifying complex meanings, and it does not benefit from adding certain types of complexity to the analysis. Our ML experiments show that in one ontology which is a simple typology, against expectations, semantic opposites attract each other and support the taxonomic structure of the other. And finally, we found some evidence that institutional factors do influence how well our taxonomy of uncertainty performs, but its ability to capture meaning does not vary greatly across the time, institutional context, and cultures we investigated.

Gangs of Russia: From the Streets to the Corridors of Power

levels of incarceration, and which might offer insight on the relative importance of a category like ideology. A more striking limit of the method is its inability to provide much help with the normative questions surrounding the core elements of the study: access and justice. Skirting the question of whether, in fact, having a dispute heard in court means access to justice allows the historical side of the story to be told, to be sure. A different method may be needed to assess just how effective courts are regarding workers’ rights with respect to companies like Walmart, for example, or high school students’ privacy rights on school grounds. If a dispute is resolved informally, is this to be considered a loss of access or of justice simply by definition? Is it always a loss of one or both of these, or only under some kinds of circumstances, with some sorts of outcomes? These are crucial questions, for example, in assessing the value of informal restorative justice efforts in schools. While methodological, these are no small quibbles because they are at the core of what we study when we study law and politics and society. And yet to pursue these more comparative and more normatively inflected studies with any degree of success, we also need the kind of sober, careful institutional history Staszak has provided in her excellent first book.