This paper is a contribution to the theorization of regulatory compliance. It presents a framework for analyzing regulatees' responses to public regulation. It identifies the main variables and mechanisms through which regulatory policy may influence regulatees' preferences. Thus, it provides a theory for interpreting compliance and noncompliance In the first semester of 2010, Greece was at the centre of public attention. Its huge public debt had made it the plaything of financial markets, and appeared to threaten the equilibrium of the whole Euro-zone. Questions were quickly raised as to why Greece had become so indebted. Journalists and academics uncovered several causes, but they found a particularly striking one in the pervasiveness of tax evasion, one of the various ways in which ingrained dispositions for noncompliance manifest themselves in Greek society.According to conservative estimates, unpaid taxes have amounted to an average of 20 billion Euros per year. Paradoxically, in the previous decade Greece had become increasingly wealthy, while state revenue has decreased.

Responsive Regulation translated an ongoing academic debate about behavior orientation and regulatory enforcement into a synthetic framework. Yet ethnographic studies reveal that ambiguity pervades regulator-regulatee interactions and suggest that the reality of regulatory encounters may be too ambivalent to fit the picture of the regulatory "game" at the heart of Ayres and Braithwaite's theory. This article proposes to address this ambivalence by drawing the outline of a relational signaling approach to regulatory encounters. The regulatory game is deconstructed into several ideal types of regulator-regulatee relationships. Within each ideal type ambiguity is managed with relational signals, namely behaviors that take a specific signification depending on the nature of the relationship. A relational signaling approach can account for the varying meanings of cooperation, defection, and mutual social control across different regulator-regulatee dyads.

Detecting non-compliant behaviors is an important step in the enforcement of regulations. The literature on the subject is vast yet also narrow in its approach, in the sense that it has built on the assumption that regulators would always want to maximize information quantity and quality, while acting under two fundamental constraints: the regulator's resources, and the information asymmetry between regulator and regulatee. This article argues that regulatory agencies might not always want to maximize information: rather, other bureaucratic goals and concerns might shape detection preferences and toolsof detection in what might seem to be unexpected and irrational ways. To support this argument, the article presents a case study of the regulation of industrial risks in France, which focuses on the detection of incidents -small losses of control -taking place in high hazard sites. The study presents a rich set of observations. It finds regulators sharing with regulatees a restrictive interpretation of incident reporting obligations. It identifies also a range of third party informants -employees -who have been 2 neither solicited by regulators to contribute to detecting incidents, nor have been particularly well received by them when they have done so. The motives of regulators that can account for these detection preferences are mixed, but an overarching one appears to be their concerns for reputational risk. Seen through such a lens, certain types of incidents and certain tools of detecting them appeared either unimportant, or on the contrary as deserving attention and effort. On the basis of the case study and other empirical illustrations found in the literature, the article then offers a more general argument about how bureaucratic reputation may shape detection practices in business regulation.

Tackling antimicrobial resistance (AMR) in animal farming and its impact on public health is a key priority for EFSA and other public health authorities in Europe. This study is a contribution to the joint effort by EFSA and EU member state authorities to address the issue. Focusing on the EU farming sector, it documents perceptions of the risks of AMR, associated behaviours, and the reasons and rationales behind them. Consumers, veterinarians and (pig and poultry) farmers in a sample of European countries were surveyed using a combination of methods (online survey, semi-structured interviews). The evidence gathered can inform communication strategies at national and EU level to increase awareness where these are designed to inform risk perception and change behaviours in relation to the use of antibiotics in animal farming and antibiotics' impact on human health. © ICF, 2017

The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therapeutic biomarkers for specific diseases. Several data sources are continuously integrated to provide the user with up-to-date information on current research in this field. State-of-theart text mining technologies are deployed to provide added value on top of the original content, including named entity detection, relation extraction, classification, clustering, ranking, summarization, and visualization. We present two novel technologies that are related to the analysis of temporal dynamics of text archives and associated ontologies. Currently, the MeSH ontology is used to annotate the scientific articles entering the PubMed database with medical terms. Both the maintenance of the ontology as well as the annotation of new articles is performed largely manually. We describe how probabilistic topic models can be used to annotate recent articles with the most likely MeSH terms. This provides our users with a competitive advantage because, when searching for MeSH terms, articles are found long before they are manually annotated. We further present a study on how to predict the inclusion of new terms in the MeSH ontology. The results suggest that early prediction of emerging trends is possible. The trend ranking functions are deployed in our system to enable interactive searches for the hottest new trends relating to a disease.