Discourse network analysis: policy debates as dynamic networks

Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed

Gold Rush Antiques: A Database Management Case

Gold Rush Antiques is a real-world database management case. Gold Rush is a business with multiple locations across north Georgia which has experienced growth. The scenario engages students in the design and development of a database to advance the organization and analysis of the data about dealers, employees, products, and sales transactions. This case is created at various levels of data management coursework – beginning, intermediate, or advanced. The case scenario is written at a beginner level; teaching notes have intermediate and advanced suggestions (provided upon request). Students are requested to develop a working prototype of a database management system that includes the design of data, tables, forms, queries, and reports. The Gold Rush Antiques case study allows students to not only learn the development of a database but also understand how to examine, analyze, and apply business procedures. To assist the instructor, sample data is provided in the Appendix

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

Patient autonomy and choice in healthcare: Self-testing devices as a case in point

This paper aims to critique the phenomenon of advanced patient autonomy and choice in healthcare within the specific context of self-testing devices. A growing number of self-testing medical devices are currently available for home use. The premise underpinning many of these devices is that they assist individuals to be more autonomous in the assessment and management of their health. Increased patient autonomy is assumed to be a good thing. We take issue with this assumption and argue that self-testing provides a specific example how increased patient autonomy and choice within healthcare might not best serve the patient population. We propose that current interpretations of autonomy in healthcare are based on negative accounts of liberty to the detriment of a more relational understanding. We also propose that Kantian philosophy is often applied to the healthcare arena in an inappropriate manner. We draw on the philosophical literature and examples from the self-testing process to support these claims. We conclude by offering an alternative account of autonomy based on the interrelated concepts of relationality, care and responsibility

Gender Discrimination in Data Analysis: a Socio-Technical Approach

International audienceTechnology characterizes and facilitates our daily lives, but its pervasive use can result in the introduction or the exacerbation of social problems. Because of their intrinsic complexity, these issues require to be addressed from different but complementary perspectives, which are provided to us by two disciplines of very different nature: data science and sociology. Specifically, this thesis would like to be a bridge between the technical field of data analysis and a specific category of social problems, namely that of discrimination, and, in particular, gender discrimination.To move within this context, we use an approach that has data analysis as its starting point, and which finds in sociology a useful supporting instrument, as well as a source of requirements. We investigate in depth the sociological reasons behind gender discrimination in the specific society of our interest – the American one – introducing and exploring what is commonly referred as ‘gender gap’, and we carry out several experiments on data related to U.S. employees, focusing on the economic perspective (gender pay gap) but taking into account the different other facets of the problem.The main contributions of this thesis derive from the application of preprocessing techniques and the use of tools created with the aim of detecting bias in data, with which we try to understand which design choices have the greatest impact on the so-called ‘fairness’ of the results, and of which we highlight strengths and weaknesses, emphasizing the importance of a multidisciplinary approach to problems of this kind, that is essential to obtain information on the complex context in which data are embedded