Suchergebnisse

Secondary data analysis has become central to the research experience of many, if not the majority, of sociologists. The problem with this approach to research is the subtle, but powerful, message that is conveyed about the nature of the social world. Social existence is portrayed in an abstract and somewhat dehumanizing manner. Additionally, due to this social imagery, researchers are not encouraged to enter communities to conduct research. As a result, many methodological skills that used to be learned, almost by osmosis, are no longer emphasized during the research enterprise.

Post-panopticism is aligned with the Foucauldian conception of power and illustrates its apparatuses and mechanisms, for instance the visibility of bodies under the gaze, the facility to mobilise power relations for political purposes, and the capacity to engage self -technologies where there is self-surveillance and surveillance of others. As a concept, it is a confluence of the disciplinary power of panoptic control and the ubiquitous security mechanism of biopower in action. Post-panopticism in the discipline provides a means to identify areas of lack in teacher and student populations. Post-panoptic surveillance in Australian schools is illustrated in this article around the use of data wall displays. Data walls are a collective mechanism that produces biopower through its alignment with panoptic disciplinary power in schools. These data assemblages are an example of a suite of technologies that profile student performance, mould teaching practices, and shape subjectivities of leaders, teachers, and students.

The combined set of skills needed for producing data journalism (e.g., investigative journalism methods, programming, knowledge in statistics, data management, statistical reporting, and design) challenges the understanding of what competences a journalist needs and the boundaries for the tasks journalists perform. Scholars denote external actors with these types of knowledge as interlopers or actors at the periphery of journalism. In this study, we follow two Swedish digital native data journalism start-ups operating in the Nordics from when they were founded in 2012 to 2019. Although the start-ups have been successful in news journalism over the years and acted as drivers for change in Nordic news innovation, they also have a presence in sectors other than journalism. This qualitative case study, which is based on interviews over time with the start-up founders and a qualitative analysis of blog posts written by the employees at the two start-ups, tells a story of journalists working at the periphery of legacy media, at least temporarily forced to leave journalism behind yet successfully using journalistic thinking outside of journalistic contexts.

The interaction between information, innovation and market outcomes is shaping the modern digital industries of the 21st century: the business models of search engines, social networks, e-commerce websites and marketplaces are highly reliant on the ability to gather and process large amount of data. At the same time, it is increasingly recognized that the use of Big Data by online platforms and intermediaries has far-reaching consequences not only on economic activity, but also on social and political mechanisms: technological developments affecting information flows affect the organization of markets as well as the nature of individual interactions and the functioning of the political process. A set of complementary policy tools is needed to define a comprehensive governance of online markets that effectively protects competition, consumers as well as individuals' privacy and media pluralism. Not all sensitive issues raised by Big Data are also competition issues. However, because of the high degree of concentration that characterizes online markets, antitrust policy finds itself at the crossroad between Big Data and the transformative effects that the Internet is having on the economy and on society. Antitrust enforcement is well equipped and sufficiently flexible to adapt its analytical tools to deal effectively with potential data-driven competition problems and thus contribute to the economic governance of digital market. This article focuses on the implications that Big Data have for antitrust enforcement, i.e. on the potential application of competition law to those (pathological) situations in which Big Data might be used by a dominant company to foreclose competitors or to exploit consumers, might be a relevant factor in the assessment of mergers' anticompetitive effects or might facilitate collusive behavior.

There is a growing tendency to release all sorts of public data on the Internet. The greater availability of interoperable public data catalyses secondary use of such data, which leads to growth of information industries and better government transparency. Open data policies may, nevertheless, be in conflict with the individual's right to information privacy as protected by the Data Protection Directive. This directive sets rules to the processing of personal data in the European Union. Technological developments and the increasing amount of publicly available data are, however, blurring the lines between non-personal and personal data. Open data may not seem to be personal data on first glance especially when it is anonymised or aggregated. However, it may become personal data by combining it with other publicly available data or when it is de-anonymised. In this article, we argue that these developments extend the reach of European Union privacy regulation to open data, which may obstruct the implementation of open data policies in the European Union. ; OTB Research Institute for the Built Environment

Medical tourism is popular within the Association of Southeast Asian Nations (ASEAN) region, especially in the Indonesian region. Theeconomic prosperity, closer vicinity as compared to Jakarta, and the quality of medical services provided in the neighbouring countrieslead to medical tourism prospering among the Indonesians. Malaysia is one of the most frequently visited countries by Indonesians asmedical tourists. The growth of medical tourism triggers issues of the adequacy of the Indonesian and Malaysian laws to regulatecross-border medical records. It is the aim of this study to examine the adequacy of the current laws in handling cross-border medical records. This study applied doctrinal legal research methodology, i.e., mainly library-based research, where the main legal materials were from Malaysia and Indonesia. It was found that the need for personal data protection is a necessity since boundaries among jurisdictions are becoming "borderless". The Malaysian law, although comprehensive, has yet to gazette any country as a "whitelisted country" to allow for cross-border data. The Indonesian law does not regulate crossborder medical records. The risk of personal data leakage has become imminent. The importance of cross-border medical records protection is important to create safe integrated medical records. While Malaysia has enacted a comprehensive legal framework on personal data protection (including medical data), Indonesia needs to enhance its legal framework in protecting the data. Regionally, the legal framework of cross-border personal data between Malaysia and Indonesia should be updated in accordance with the ASEAN Data Management Framework.

Perceptive information about users of the social networks should be protected. The confront is to plan methods to publish social network data in a form that affords usefulness without compromising privacy. Previous research has proposed a variety of privacy models with the corresponding protection mechanisms that put off both unintentional private information escape and attacks by malicious adversaries. These early privacy models are mainly disturbed with identity and link revelation. The social networks are modelled as graphs in which users are nodes and social connections are edges. The intimidation definitions and defence mechanisms leverage structural properties of the graph. This paper is stimulated by the recognition of the need for a better grain and more personalized privacy. Users commend social networks such as Face book and LinkedIn with a wealth of personal information such as their age, address, current location or political orientation. We refer to these details and messages as features in the user's profile. We propose a privacy protection scheme that not only put off the revelation of identity of users but also the disclosure of selected features in users' profiles.

Introduction/purpose: The digitization of healthcare has gained particular importance in the years since the emergence of COVID-19 and also has become one of the primary goals of the Government of the Republic of Serbia. Telemedicine is a good solution when the patient cannot come to a healthcare facility. Mobile healthcare applications are already widely used, but in both fields the important challenge is data security. The aim of this paper is to review solutions for data security in mobile healthcare from the technical side and possible challenges in the process of digitization of the healthcare system in Serbia. Methods: This review is based on current papers in this area, on the available relevant literature and the authors' many years of experience in this field. Experiences in the process of digitization of healthcare in Serbia are based on available articles and regulations. Finally, possible challenges are presented from the authors' perspective based on everything presented in the field of data security in mobile healthcare. Results: The analysis of the papers reviewed from the point of view of data security showed that users are often ready to sacrifice their privacy for the sake of convenience provided by mobile applications. Conclusion: Based on the review of the papers and clear data security requirements that include the presented safeguards, one of the main tasks of the entire community is to raise awareness of information security and awareness of the need for cyber hygiene of each individual, which is the basis for the safe use of e-health services.

Data linkage traditionally uses deterministic and probabilistic methods. Alternatively, machine learning methods can be applied as classification algorithms, using the data to inform decisions. This project compared the quality, in terms of precision and recall, of traditional methods with selected machine learning methods when applied to a standard linkage problem.
Two supervised methods, gradient boosted trees (GBT) and multiple layered perceptron classifier (MLPC), and one unsupervised method, maximum entropy classification (MEC), were implemented. The England and Wales 2021 Census to Census Coverage Survey (CCS) linkage was used as a gold-standard (GS) linked dataset to provide training samples for the supervised methods as well as testing samples for all methods. The F1 score (harmonic mean of precision and recall) was used to compare the performance of the models and to determine the optimal parameters and thresholds.
The Splink implementation of Fellegi-Sunter with Expectation Maximisation was used as a baseline for comparison.
The methods, trained on a sample of the GS, were used to link census and CCS data. All methods performed well with MEC achieving the highest precision (99.79%) but lowest recall (96.36%). The MLPC model achieved the highest F1 score (98.94%).
To understand the implications of not retraining supervised models for each dataset, the models were also used to link Census to a health dataset. The supervised models were not retrained using the health data; instead, the optimised GS models were applied. MEC had the lowest precision (96.51%) but the highest recall (98.48%) and highest F1 score (97.49%). With F1 scores of 96.99% and 96.14% respectively, the GBT and MLPC supervised models were not far behind in performance, despite not being trained using health data.
We have shown that machine learning methods can be used effectively for data linkage problems. Unsurprisingly, supervised models perform best when trained on and applied to the same data. Further research into generic training may allow us to use both supervised and unsupervised machine learning models for future data linkage.

In this article, we discuss on a general and mainly theoretical-conceptual level how schools and societies can/should respond to Big Data. Firstly, we identify what Big Data is. Based on Levi Bryant's onto-cartography, we suggest that Big Data can be characterized ontologically as different socio-technical mechanic assemblages. These assemblages comprise different combinations of social systems, big states and big companies, and Big Data technologies on the one hand; and citizens, not least teachers and students, on the other hand. Secondly, we present three different assembling scenarios: 1) a state model, where a Big State in alliance with new technology (and companies) uses Big Data to control citizens and students; 2) a market model, where Big Companies in alliance with new technology (and politicians) are free to use Big Data to nudge citizens and students; 3) a democratic model, where citizens and students are protected by the state from being surveilled, controlled and nudged by new big Cyborgs and also educated to be critical of and act with and against Big Data.

Data shapes the development of Artificial Intelligence (AI) as we currently know it, and for many years centralized networking infrastructures have dominated both the sourcing and subsequent use of such data. Research suggests that centralized approaches result in poor representation, and as AI is now integrated more in daily life, there is a need for efforts to improve on this. The AI research community has begun to explore managing data infrastructures more democratically, finding that decentralized networking allows for more transparency which can alleviate core ethical concerns, such as selection-bias. With this in mind, herein, we present a mini-survey framed around data representation and data infrastructures in AI. We outline four key considerations (auditing, benchmarking, confidence and trust, explainability and interpretability) as they pertain to data-driven AI, and propose that reflection of them, along with improved interdisciplinary discussion may aid the mitigation of data-based AI ethical concerns, and ultimately improve individual wellbeing when interacting with AI.

Presentation given at the UCT Open Data Day 2021. The presentation focusses on the the work done on the Open Data South Africa project.