Bringing the Law to the Library: The Importance of Librarian Mediation in Access to Justice Services

Library access to justice programs and services help people who need legal information and who cannot afford an attorney. Librarian mediation is a critical component in the provision of access to justice services. However, the value of library mediation, or assistance with using library resources, is often unrecognized, particularly where members of the public are trying to access electronic legal information sources, online legal forms, and other law technologies. This article will explore the role of librarians in providing access to justice services from the perspective of the work of Richard Susskind, which emphasizes technological approaches to providing legal services. While there is a place for technology in access to justice services, there is also a valuable role that librarians play in contributing to access to justice.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.     

Google Scholar and Web of Science: Examining gender differences in citation coverage across five scientific disciplines

Many studies demonstrate differences in the coverage of citing publications in Google Scholar (GS) and Web of Science (WoS). Here, we examine to what extent citation data from the two databases reflect the scholarly impact of women and men differently. Our conjecture is that WoS carries an indirect gender bias in its selection criteria for citation sources that GS avoids due to criteria that are more inclusive. Using a sample of 1250 U.S. researchers in Sociology, Political Science, Economics, Cardiology and Chemistry, we examine gender differences in the average citation coverage of the two databases. We also calculate database-specific h-indices for all authors in the sample. In repeated simulations of hiring scenarios, we use these indices to examine whether women's appointment rates increase if hiring decisions rely on data from GS in lieu of WoS. We find no systematic gender differences in the citation coverage of the two databases. Further, our results indicate marginal to non-existing effects of database selection on women's success-rates in the simulations. In line with the existing literature, we find the citation coverage in WoS to be largest in Cardiology and Chemistry and smallest in Political Science and Sociology. The concordance between author-based h-indices measured by GS and WoS is largest for Chemistry followed by Cardiology, Political Science, Sociology and Economics.