Understanding the role of open peer review and dynamic academic articles

We welcome the commentary by L. Egghe stimulating discussion on our recent article “Natural selection of academic papers” (NSAP) that focuses on an important modern issue at the heart of the scientific enterprise — the open and continuous evaluation and evolution of research. We are also grateful to the editor of Scientometrics for giving us the opportunity to respond to some of the arguments by L. Egghe that we believe are inaccurate or require further comment

EMI in Chinese higher education: the Muddy water of 'Englishisation'

English in Chinese higher education has shifted from being taught as a foreign language alongside other disciplinary-focussed courses to becoming an important medium of instruction used for learning and teaching non-language related academic subjects. While using English medium instruction (EMI) seems a natural and neutral academic exercise, the switch has muddied the water of EMI implementation and caused a number of social and academic issues for both students and lecturers. These problems include unfair promotion opportunities, unequal access to EMI classes, inadequate learning outcomes, and poor teaching quality. This special issue builds on past and current EMI work that explores issues related to EMI implementation in Chinese higher education institutions and in classrooms. Through the selection of several empirical papers, the special issue shines light on current knowledge, policies and practices of EMI in China to pave the way for research-informed recommendations

Open science and modified funding lotteries can impede the natural selection of bad science.

Assessing scientists using exploitable metrics can lead to the degradation of research methods even without any strategic behaviour on the part of individuals, via 'the natural selection of bad science.' Institutional incentives to maximize metrics like publication quantity and impact drive this dynamic. Removing these incentives is necessary, but institutional change is slow. However, recent developments suggest possible solutions with more rapid onsets. These include what we call open science improvements, which can reduce publication bias and improve the efficacy of peer review. In addition, there have been increasing calls for funders to move away from prestige- or innovation-based approaches in favour of lotteries. We investigated whether such changes are likely to improve the reproducibility of science even in the presence of persistent incentives for publication quantity through computational modelling. We found that modified lotteries, which allocate funding randomly among proposals that pass a threshold for methodological rigour, effectively reduce the rate of false discoveries, particularly when paired with open science improvements that increase the publication of negative results and improve the quality of peer review. In the absence of funding that targets rigour, open science improvements can still reduce false discoveries in the published literature but are less likely to improve the overall culture of research practices that underlie those publications

Modeling and Analysis of Scholar Mobility on Scientific Landscape

Scientific literature till date can be thought of as a partially revealed landscape, where scholars continue to unveil hidden knowledge by exploring novel research topics. How do scholars explore the scientific landscape , i.e., choose research topics to work on? We propose an agent-based model of topic mobility behavior where scholars migrate across research topics on the space of science following different strategies, seeking different utilities. We use this model to study whether strategies widely used in current scientific community can provide a balance between individual scientific success and the efficiency and diversity of the whole academic society. Through extensive simulations, we provide insights into the roles of different strategies, such as choosing topics according to research potential or the popularity. Our model provides a conceptual framework and a computational approach to analyze scholars' behavior and its impact on scientific production. We also discuss how such an agent-based modeling approach can be integrated with big real-world scholarly data.Comment: To appear in BigScholar, WWW 201

Performance Analysis of Selection Combining Over Correlated Nakagami-m Fading Channels with Constant Correlation Model for Desired Signal and Cochannel Interference

A very efficient technique that reduces fading and channel interference influence is selection diversity based on the signal to interference ratio (SIR). In this pa¬per, system performances of selection combiner (SC) over correlated Nakagami-m channels with constant correlation model are analyzed. Closed-form expressions are obtained for the output SIR probability density function (PDF) and cumulative distribution function (CDF) which is main contribution of this paper. Outage probability and the average error probability for coherent, noncoherent modulation are derived. Numerical results presented in this paper point out the effects of fading severity and cor¬relation on the system performances. The main contribu¬tion of this analysis for multibranch signal combiner is that it has been done for general case of correlated co-channel interference (CCI)