Taking reasonable pluralism seriously: an internal critique of political liberalism

The later Rawls attempts to offer a non-comprehensive, but nonetheless moral justification in political philosophy. Many critics of political liberalism doubt that this is successful, but Rawlsians often complain that such criticisms rely on the unwarranted assumption that one cannot offer a moral justification other than by taking a philosophically comprehensive route. In this article, I internally criticize the justification strategy employed by the later Rawls. I show that he cannot offer us good grounds for the rational hope that citizens will assign political values priority over non-political values in cases of conflict about political matters. I also suggest an alternative approach to justification in political philosophy (that is, a weak realist, Williams-inspired account) that better respects the later Rawls’s concern with non-comprehensiveness and pluralism than either his own view or more comprehensive approaches. Thus, if we take reasonable pluralism seriously, then we should adopt what Shklar aptly called ‘liberalism of fear’. </jats:p

An evaluation of Bradfordizing effects

The purpose of this paper is to apply and evaluate the bibliometric method Bradfordizing for information retrieval (IR) experiments. Bradfordizing is used for generating core document sets for subject-specific questions and to reorder result sets from distributed searches. The method will be applied and tested in a controlled scenario of scientific literature databases from social and political sciences, economics, psychology and medical science (SOLIS, SoLit, USB Köln Opac, CSA Sociological Abstracts, World Affairs Online, Psyndex and Medline) and 164 standardized topics. An evaluation of the method and its effects is carried out in two laboratory-based information retrieval experiments (CLEF and KoMoHe) using a controlled document corpus and human relevance assessments. The results show that Bradfordizing is a very robust method for re-ranking the main document types (journal articles and monographs) in today’s digital libraries (DL). The IR tests show that relevance distributions after re-ranking improve at a significant level if articles in the core are compared with articles in the succeeding zones. The items in the core are significantly more often assessed as relevant, than items in zone 2 (z2) or zone 3 (z3). The improvements between the zones are statistically significant based on the Wilcoxon signed-rank test and the paired T-Test

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