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

The relationship between quality of research and citation frequency

BACKGROUND: Citation counts are often regarded as a measure of the utilization and contribution of published articles. The objective of this study is to assess whether statistical reporting and statistical errors in the analysis of the primary outcome are associated with the number of citations received. METHODS: We evaluated all original research articles published in 1996 in four psychiatric journals. The statistical and reporting quality of each paper was assessed and the number of citations received up to 2005 was obtained from the Web of Science database. We then examined whether the number of citations was associated with the quality of the statistical analysis and reporting. RESULTS: A total of 448 research papers were included in the citation analysis. Unclear or inadequate reporting of the research question and primary outcome were not statistically significantly associated with the citation counts. After adjusting for journal, extended description of statistical procedures had a positive effect on the number of citations received. Inappropriate statistical analysis did not affect the number of citations received. Adequate reporting of the primary research question, statistical methods and primary findings were all associated with the journal visibility and prestige. CONCLUSION: In this cohort of published research, measures of reporting quality and appropriate statistical analysis were not associated with the number of citations. The journal in which a study is published appears to be as important as the statistical reporting quality in ensuring dissemination of published medical science

Tracing the wider impacts of biomedical research: A literature search to develop a novel citation categorisation technique

There is an increasing need both to understand the translation of biomedical research into improved healthcare and to assess the range of wider impacts from health research such as improved health policies, health practices and healthcare. Conducting such assessments is complex and new methods are being sought. Our new approach involves several steps. First, we developed a qualitative citation analysis technique to apply to biomedical research in order to assess the contribution that individual papers made to further research. Second, using this method, we then proposed to trace the citations to the original research through a series of generations of citing papers. Third, we aimed eventually to assess the wider impacts of the various generations. This article describes our comprehensive literature search to inform the new technique. We searched various databases, specific bibliometrics journals and the bibliographies of key papers. After excluding irrelevant papers we reviewed those remaining for either general or specific details that could inform development of our new technique. Various characteristics of citations were identified that had been found to predict their importance to the citing paper including the citation’s location; number of citation occasions and whether the author(s) of the cited paper were named within the citing paper. We combined these objective characteristics with subjective approaches also identified from the literature search to develop a citation categorisation technique that would allow us to achieve the first of the steps above, i.e., being able routinely to assess the contribution that individual papers make to further research.Medical Research Council as part of the MRC-NIHR Methodology Research Programme, and Professor Martin Buxton

A Conserved Stem Loop Motif in the 5′Untranslated Region Regulates Transforming Growth Factor-β1 Translation

Transforming growth factor-β1 (TGF-β1) regulates cellular proliferation, differentiation, migration, and survival. The human TGF-β1 transcript is inherently poorly translated, and translational activation has been documented in relation to several stimuli. In this paper, we have sought to identify in cis regulatory elements within the TGF-β1 5′Untranslated Region (5′UTR). In silico analysis predicted formation of stable secondary structure in a G/C-rich element between nucleotides +77 to +106, and demonstrated that this element is highly conserved across species. Circular dichroism spectroscopy confirmed the presence of secondary structure in this region. The proximal 5′UTR was inhibitory to translation in reporter gene experiments, and mutation of the secondary structure motif increased translational efficiency. Translational regulation of TGF-β1 mRNA is linked to altered binding of YB-1 protein to its 5′UTR. Immunoprecipitation-RT-qPCR demonstrated a high basal association of YB-1 with TGF-β1 mRNA. However, mutation of the secondary structure motif did not prevent interaction of YB-1 with the 5′UTR, suggesting that YB-1 binds to this region due to its G/C-rich composition, rather than a specific, sequence-dependent, binding site. These data identify a highly conserved element within the TGF-β1 5′UTR that forms stable secondary structure, and is responsible for the inherent low translation efficiency of this cytokine

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