From dignity to security protocols: a scientometric analysis of digital ethics

Our lives are increasingly intertwined with the digital realm, and with new technology, new ethical problems emerge. The academic field that addresses these problems—which we tentatively call ‘digital ethics’—can be an important intellectual resource for policy making and regulation. This is why it is important to understand how the new ethical challenges of a digital society are being met by academic research. We have undertaken a scientometric analysis to arrive at a better understanding of the nature, scope and dynamics of the field of digital ethics. Our approach in this paper shows how the field of digital ethics is distributed over various academic disciplines. By first having experts select a collection of keywords central to digital ethics, we have generated a dataset of articles discussing these issues. This approach allows us to generate a scientometric visualisation of the field of digital ethics, without being constrained by any preconceived definitions of academic disciplines. We have first of all found that the number of publications pertaining to digital ethics is exponentially increasing. We furthermore established that whereas one may expect digital ethics to be a species of ethics, we in fact found that the various questions pertaining to digital ethics are predominantly being discussed in computer science, law and biomedical science. It is in these fields, more than in the independent field of ethics, that ethical discourse is being developed around concrete and often technical issues. Moreover, it appears that some important ethical values are very prominent in one field (e.g., autonomy in medical science), while being almost absent in others. We conclude that to get a thorough understanding of, and grip on, all the hard ethical questions of a digital society, ethicists, policy makers and legal scholars will need to familiarize themselves with the concrete and practical work that is being done across a range of different scientific fields to deal with these questions.     

An empirical investigation of the tribes and their territories: Are research specialisms rural and urban?

We propose an operationalization of the rural and urban analogy introduced in Becher and Trowler (2001). According to them, a specialism is rural if it is organized into many, smaller topics of research, with higher author mobility among them, lower rate of collaboration and productivity, lower competition for resources and citation recognitions compared to an urban specialism. It is assumed that most humanities specialisms are rural while science specialisms are in general urban: we set to test this hypothesis empirically. We first propose an operationalization of the theory in most of its quantifiable aspects. We then consider specialisms from history, literature, computer science, biology, astronomy. Our results show that specialisms in the humanities present a sensibly lower citation and textual connectivity, in agreement with their organization into more, smaller topics per specialism, as suggested by the analogy. We argue that the intellectual organization of rural specialisms might indeed be qualitative different from urban ones, discouraging the straightforward application of citation-based indicators commonly applied to urban specialisms without a dedicated re-design in acknowledgement of these differences.     

The role of domain knowledge in cognitive modeling of information search

Computational cognitive models developed so far do not incorporate individual differences in domain knowledge in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having a low (non-expert semantic space) and a high (expert semantic space) amount of medical and health related information to represent respectively low and high knowledge of users in this domain. We also investigated two different processes along which one can gain a larger amount of knowledge in a domain: an evolutionary and a common core process. Simulations of model click behavior on difficult information search tasks and subsequent matching with actual behavioral data from users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling for high domain knowledge participants (in terms of the number of matches between the model predictions and the actual user clicks on search result pages) was higher with the expert semantic space compared to the non-expert semantic space while for low domain knowledge participants it was the other way around. When the process of knowledge acquisition was taken into account, the effect of using a semantic space based on high domain knowledge was significant only for high domain knowledge participants, irrespective of the knowledge acquisition process. The implications of these outcomes for support tools that can be built based on these models are discussed.     

The effect of distributed practice on students’ conceptual understanding of statistics

In this study the effect of the reduced distribution of study activities on students’ conceptual understanding of statistics is investigated in a quasi-experiment. Conceptual understanding depends on coherent and error free knowledge structures. Students need time to construct such knowledge structures. A curriculum reform at our university resulted in statistics courses which were considerably shortened in time, thereby limiting students’ possibility to distribute study activities. Independent samples of students from before and after the reform were compared. To gauge conceptual understanding of statistics, students answered open ended questions in which they were asked to explain and relate important statistical concepts. It was shown that the reduction of distributed practice had a negative effect on students’ understanding. The finding that condensed courses make it more difficult for students to reach proper understanding of the subject matter is of interest for anyone who is engaged in reforming curricula or designing courses.     

Analysis of Behavioral and Emotional Problems in Children Highlights the Role of Genotype × Environment Interaction

This study tested for Genotype × Environment (G × E) interaction on behavioral and emotional problems in children using new methods that do not require identification of candidate genes or environments, can distinguish between interaction with shared and unique environment, and are insensitive to scale effects. Parental ratings of problem behavior from 14,755 twin pairs (5.3 years, SD = 0.22) indicated G × E interaction on emotional liability, social isolation, aggression, attention problems, dependency, anxiety, and physical coordination. Environmental influences increased in children who were genetically more predisposed to problem behavior, with ~20% of the variance due to G × E interaction (8% for anxiety to 37% for attention problems). Ignoring G × E interaction does not greatly bias heritability estimates, but it does offer a comprehensive model of the etiology for childhood problems.