Diversity in economics:an analysis of journal quality perceptions

It is still debatable whether scientific diversity is a virtue or a disadvantage for the development of a discipline. Nonetheless, diversity among scientists with respect to their journal quality perceptions plays an important role in hiring and promotion decisions. In this article we examine the degree of diversity within economics based on the journal quality perceptions of 2,103 AEA economists worldwide. Specifically, we empirically test for factors that might explain differences in an economist's journal quality perceptions. These factors include an economist's geographic origin, school of thought, journal affiliation, field of specialization and research orientation. Indeed, we find that a significant degree of diversity in journal quality perceptions exists between economists that belong in different subgroups. These results might explain the frequent debates in tenure and promotion committees where journal standings are used for the evaluation of a researcher's output.

Pharmacy (ISSN 2226-4787):a journal of pharmacy education and practice

A journal of pharmacy education and practice is an international scientific open access journal on pharmacy education and practice, and is published by MDPI online quarterly. The practice of pharmacy is changing at an unprecedented rate as the profession moves from a focus upon preparation and supply of medicines to a clinical patient-facing role. While an understanding of the science related to medicines remains core to pharmacy education, the changes in practice are driving changes to the traditional methods of pharmacy education. This is reflected at an international level by major changes in the educational standards set by statutory regulators and by policy statements from bodies such as the World Health Organisation. These changes reflect an increasing trend to look at educational policy at a supra-national level, typified by the “Pharmine Project” led by the Association of European Faculties of Pharmacy.

Public scientific communication on Twitter:visual analytic approach

Purpose - The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics. Design/methodology/approach - The high-dimensional visualisation approach was applied to three scientific topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics. Findings - The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter. Research limitations/implications - Three topics are studied and these illustrate a range of frames, but results may not be representative of all scientific topics. Social implications - Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large. Originality/value - This study differs from standard approaches to the analysis of microblog data, which tend to focus on machine driven analysis large-scale datasets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.