What is corpus linguistics? What the data says

Stubbs (2006), in his state of the art overview, draws attention to the frequent reticence or vagueness of corpus analysts in discussing their operational methods within a scientific context, (a context addressed in detail in Partington forthcoming). This lack of clarity in discussing the methodological framework employed is, perhaps, most surprising given the way in which corpus linguistics situates itself within a scientific frame, and lays such claims to a scientific nature. This brief paper, then, addresses the question posed in its title, namely, “What is corpus linguistics?” – is it a discipline, a methodology, a paradigm or none or all of these? – but does not attempt to offer any definitive answers. Rather, the aim is to present the reader with a number of observations on how corpus linguistics has been construed in its own literature and then to leave the question open, in the hope of stimulating further discussion. The study takes the specific term corpus linguistics and looks at how it is defined and described both explicitly and implicitly in a variety of relevant sources

Data Services in Academic Libraries – What Strange Beast Is This?

Data services, though a longstanding specialization, is a fast-growing and in-demand niche in the academic librarian job market. That said, it is still somewhat of a mystery to many outside of a small circle in academic librarianship. This essay attempts to remedy this mystification. The author gives an overview of data services librarianship, using examples from her San José State University INFO 220-12 class, “Data Services in Libraries,” to illustrate the core aspects and activities of this specialization in academic libraries. In so doing, she elucidates how this specialization is at once a natural extension of established roles for academic librarians and also an opportunity for librarians to expand their roles for increased relevancy in the higher education research enterprise

What is new about global corporations? Interpreting statistical data on corporate internationalization

In the debate on globalization it is often argued that multinational corporations (MNCs) have gained increasing power due to their growth and due to new global or transnational structures and strategies. This paper presents empirical evidence especially from various national statistics ignored in the debate so far that contradicts these assumptions and allow a deeper understanding of the development of the structures of MNCs. These data indicate that foreign direct investment (FDI) is not a good indicator for the real growth of MNCs. Rising prices of cross-border M&A transactions lead to high growth-rates of FDI while the real growth of MNCs has developed quite steadily over many decades. In manufacturing only affiliates in the periphery of MNCs home regions show an accelerated expansion (partly due to the opening-up of Eastern Europe and China). Over-all, the development of MNCs does not show significant new characteristics in the 1990s, neither in quantitative nor in qualitative terms. Despite the continuous tendency to globalize managerial coordination, truly integrated global configurations have not emerged. Network-like manufacturing structures and thus also the mobility of production are still confined regionally. The final section tries to develop an explanation for the fact that MNCs are assigned this important new role in the globalization debate. -- In der Globalisierungsdebatte wird häufig behauptet, dass multinationale Unternehmen aufgrund ihres Wachstums und aufgrund neuer globaler oder transnationaler Strukturen und Strategien zunehmende Macht gewonnen hätten. Dieses Papier präsentiert empirische Daten insbesondere von verschiedenen nationalen Statistiken, die bisher in der Debatte ignoriert worden sind und die ein besseres Verständnis der Entwicklung der Strukturen der multinationalen Konzerne erlauben. Die Daten zeigen, dass Direktinvestitionen kein guter Indikator für das Wachstum multinationaler Konzerne sind. Gestiegene Preise bei grenzüberschreitenden Mergers + Acquisitions führen zu hohen Wachstumsraten bei den Direktinvestitionen, während das reale Wachstum der multinationalen Konzerne sich seit vielen Dekaden recht kontinuierlich entwickelt. In der verarbeitenden Industrie zeigen nur die Auslandsgesellschaften in der Peripherie der jeweiligen Heimatregion der multinationalen Unternehmen ein beschleunigtes Wachstum (teilweise auch aufgrund der Öffnung Chinas und Osteuropas). Insgesamt zeigt die Entwicklung der multinationalen Konzerne in den 90er Jahren keine grundlegend neuen Eigenschaften weder in quantitativer noch in qualitativer Hinsicht. Trotz einer langfristigen Tendenz zur Globalisierung der Koordinationsstrukturen, d.h. des Managements, haben sich wirklich globale Konfigurationsstrukturen noch nicht herausgebildet: Netzwerkartige Fertigungsverbünde und damit auch die Mobilität der Produktion sind noch regional begrenzt. Der abschließende Teil dieses Papiers versucht eine Erklärung dafür zu entwickeln, dass den multinationalen Konzernen in der Globalisierungsdebatte eine bedeutende und neuartige Rolle zugeschrieben wird.

Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real-world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state-of-the-art discipline. An ignorance of observing the progression of this fast-growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics

Cryptosporidium — What is it?

Cryptosporidium is a ubiquitous enteric protozoan pathogen of vertebrates, and although recognised as a cause of disease in humans and domestic animals for over 50 years, fundamental questions concerning its biology and ecology have only recently been resolved. Overwhelming data now confirm that, like its close relatives, Cryptosporidium is a facultatively epicellular apicomplexan that is able to multiply in a host cell-free environment. These data must be considered in the context of the phylogenetic reclassification of Cryptosporidium from a coccidian to a gregarine. Together, they dictate an urgent need to reconsider the biology and behaviour of Cryptosporidium, and perhaps help to explain the parasite's incredible genetic diversity, distribution and host range. Improved imaging technologies have complemented phylogenetic studies in demonstrating the parasite's affinities with gregarine protozoa and have further supported its extracellular developmental capability and potential role as an environmental pathogen. These advances in our understanding of Cryptosporidium as a protozoan pathogen are examined with emphasis on how they may influence control strategies in the future

Data Is What Data Does: Regulating Use, Harm, and Risk Instead of Sensitive Data

Heightened protection for sensitive data is becoming quite trendy in privacy laws around the world. Originating in European Union (EU) data protection law and included in the EU’s General Data Protection Regulation (GDPR), sensitive data singles out certain categories of personal data for extra protection. Commonly recognized special categories of sensitive data include racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, health, sexual orientation and sex life, biometric data, and genetic data. Although heightened protection for sensitive data appropriately recognizes that not all situations involving personal data should be protected uniformly, the sensitive data approach is a dead end. The sensitive data categories are arbitrary and lack any coherent theory for identifying them. The borderlines of many categories are so blurry that they are useless. Moreover, it is easy to use non-sensitive data as a proxy for certain types of sensitive data. Personal data is akin to a grand tapestry, with different types of data interwoven to a degree that makes it impossible to separate out the strands. With Big Data and powerful machine learning algorithms, most non-sensitive data can give rise to inferences about sensitive data. In many privacy laws, data that can give rise to inferences about sensitive data is also protected as sensitive data. Arguably, then, nearly all personal data can be sensitive, and the sensitive data categories can swallow up everything. As a result, most organizations are currently processing a vast amount of data in violation of the laws. This Article argues that the problems with the sensitive data approach make it unworkable and counterproductive — as well as expose a deeper flaw at the root of many privacy laws. These laws make a fundamental conceptual mistake — they embrace the idea that the nature of personal data is a sufficiently useful focal point for the law. But nothing meaningful for regulation can be determined solely by looking at the data itself. Data is what data does. Personal data is harmful when its use causes harm or creates a risk of harm. It is not harmful if it is not used in a way to cause harm or risk of harm. To be effective, privacy law must focus on use, harm, and risk rather than on the nature of personal data. The implications of this point extend far beyond sensitive data provisions. In many elements of privacy laws, protections should be based on the use of personal data and proportionate to the harm and risk involved with those uses

What is the message? Perspectives on Visual Data Communication

Data visualizations are used to communicate messages to diverse audiences. It is unclear whether interpretations of these visualizations match the messages their creators aim to convey. In a mixed-methods study, we investigate how data in the popular science magazine Scientific American are visually communicated and understood. We first analyze visualizations about climate change and pandemics published in the magazine over a fifty-year period. Acting as chart readers, we then interpret visualizations with and without textual elements, identifying takeaway messages and creating field notes. Finally, we compare a sample of our interpreted messages to the intended messages of chart producers, drawing on interviews conducted with magazine staff. These data allow us to explore understanding visualizations through three perspectives: that of the charts, visualization readers, and visualization producers. Building on our findings from a thematic analysis, we present in-depth insights into data visualization sensemaking, particularly regarding the role of messages and textual elements; we propose a message typology, and we consider more broadly how messages can be conceptualized and understood