Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

The Calculus of Committee Composition

Modern institutions face the recurring dilemma of designing accurate evaluation procedures in settings as diverse as academic selection committees, social policies, elections, and figure skating competitions. In particular, it is essential to determine both the number of evaluators and the method for combining their judgments. Previous work has focused on the latter issue, uncovering paradoxes that underscore the inherent difficulties. Yet the number of judges is an important consideration that is intimately connected with the methodology and the success of the evaluation. We address the question of the number of judges through a cost analysis that incorporates the accuracy of the evaluation method, the cost per judge, and the cost of an error in decision. We associate the optimal number of judges with the lowest cost and determine the optimal number of judges in several different scenarios. Through analytical and numerical studies, we show how the optimal number depends on the evaluation rule, the accuracy of the judges, the (cost per judge)/(cost per error) ratio. Paradoxically, we find that for a panel of judges of equal accuracy, the optimal panel size may be greater for judges with higher accuracy than for judges with lower accuracy. The development of any evaluation procedure requires knowledge about the accuracy of evaluation methods, the costs of judges, and the costs of errors. By determining the optimal number of judges, we highlight important connections between these quantities and uncover a paradox that we show to be a general feature of evaluation procedures. Ultimately, our work provides policy-makers with a simple and novel method to optimize evaluation procedures

Academic misconduct, misrepresentation and gaming: a reassessment

The motivation for this Special Issue is increasing concern not only with academic misconduct but also with less easily defined forms of misrepresentation and gaming. In an era of intense emphasis on measuring academic performance, there has been a proliferation of scandals, questionable behaviors and devious stratagems involving not just individuals but also organizations, including universities, editors and reviewers, journal publishers, and conference organizers. This introduction first reviews the literature on the prevalence of academic misconduct, misrepresentation and gaming (MMG). The core of the article is organized around a life-cycle model of the production and dissemination of research results. We synthesize the findings in the MMG literature at the level of the investigator or research team, emphasizing that misbehavior extends well beyond fabrication and falsification to include behaviors designed to exaggerate or to mislead readers as to the significance of research findings. MMG is next explored in the post-research review, publication, and post-publication realms. Moving from the individual researcher to the organizational level, we examine how MMG can be engaged in by either journals or organizations employing or funding the researchers. The changing institutional environment including the growth of research assessment exercises, increased quantitative output measurement and greater pressure to publish may all encourage MMG. In the final section, we summarize the main conclusions and offer suggestions both on how we might best address the problems and on topics for future research

Deep context of citations using machine‑learning models in scholarly full‑text articles

Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy

Anti-Infective Effect of Adhesive Probiotic Lactobacillus in Fish is Correlated With Their Spatial Distribution in the Intestinal Tissue

In this study, we tested the distribution of 49 Lactobacillus strains in the mucus and mucosa of the intestine tissue of zebrafish. We observed a progressive change in the spatial distribution of Lactobacillus strains, and suggested a division of the strains into three classes: mucus type (>70% in mucus), mucosa type (>70% in mucosa) and hybrid type (others). The hybrid type strains were more efficient in protection of zebrafish against Aeromonas hydrophila infection. Three strains representing different distribution types (JCM1149, CGMCC1.2028, and JCM 20300) were selected. The mucosa type strain JCM1149 induced higher intestinal expression of inflammatory cytokines and Hsp70 than the other strains. Furthermore, we used L. rhamnosus GG and its mutant (PB22) lacking SpaCBA pili to investigate the influence of pili on spatial distribution. LGG showed a mucosa type distribution, while PB22 revealed a hybrid distribution and the disease protection was accordingly improved. The different protection ability between LGG and PB22 did not involve the intestinal microbiota, however, LGG induced injury to the mucosa of zebrafish. Collectively, the disease protection activity of Lactobacillus in zebrafish is correlated with their spatial distribution in the intestinal tissue, with strains showing a balanced distribution (hybrid type) more efficient in protection.Peer reviewe