Who's next? Research predicts museum leadership gap

A brief commentary from the Museum Association of New York (MANY) on the unprecedented gap posed by baby-boomer retirement. Based, in part, on MANY's white paper, “Who's Next? Questioning the Future of Museum Leadership in New York State,” this essay deals with questions of succession, leadership and general differences in New York's museum community.     

Another Look at What Moves Public Opinion: Media Agenda Setting and Polls in the 2000 U.S. Election

This study examined two levels of media agenda effects on aggregatepublic opinion from different news sources. The effects wereinvestigated immediately and cumulatively. Content analysisdata from the 2000 U.S. presidential election coverage by fournational news organizations were related to the Gallup pre-electionpoll standings of each candidate. Regression analyses foundthat both the salience of a candidate and the salience of theattributes of a candidate cumulatively, but not immediately,influenced his standing in the polls. An analysis of news sourcessupported the finding that the two levels of agenda-settingeffects seemed mostly cumulative rather than immediate. Newsfrom different sources, however, tended to have effects of differentdegrees and sometimes different directions on candidate pollstandings. Cumulative effects of candidate salience on aggregateopinion change were found for non-partisan and neutral newssources—reporters, poll reporting and public documents—whereasthe effects of candidate attribute salience mostly came frompartisan sources—the candidate himself and members ofthe competing political party. Possible political implicationsof these findings are discussed.     

Turning a Service Learning Experience into a Model of Student Engagement: The Lighthouse Heritage Research Connections (LHRC) Project in Hong Kong

The digital shift has provided easy access to academic library users, and yet, the usage of archival collections continues to be low. At the same time, the need for innovation in library services for cultural heritage scholarship and its advancement is emerging. This paper outlines a library-led service-learning program that connects students with lighthouse artifacts, archival collections, scholars in global academia, and wider communities. Student engagement cases are provided to illustrate the way in which the librarian utilizes the Framework of Information Literacy for Higher Education by ACRL to work closely with students. These cases also demonstrate how students can contribute to knowledge creation and preservation efforts for a specific cultural heritage topic that is not static, but which keeps receiving new contributions or additions to the depository. Thus, this paper is an answer to the ACRL's call for pilot projects to be assessed and shared with the wider community of academic librarians and support staff. It also builds on emerging roles for academic libraries like engaged learning. Librarians must move beyond simply seeing themselves as partners. Instead, librarians should see themselves as prime facilitators that co-create and co-develop cultural heritage research and historical projects by connecting actors and resources more effectively than any single actor can do alone.     

More Than Just a Student Voice: Facilitating Student Leadership Development Through the Library Student Liaison Program

This article explores how the library-student liaison program at Eastern Washington University (EWU) Libraries has evolved into an opportunity for students to develop leadership skills. Originally designed to foster better communication between the EWU Libraries and students enrolled at the university, we recognized that the process of achieving established goals for the program provided a vehicle for students' leadership-skill development. To facilitate the leadership process, librarians employed the Social Change Model. Designed specifically for college students, this model engages the student to develop individual capacities to strengthen group activities that foster positive changes in the community and society.     

Assessing information literacy instruction in interdisciplinary first year project-based courses with STEM students

Broad and discipline-specific information literacy competencies must be mastered by science, technology, engineering, and math (STEM) students. This study assessed changes in student perceptions of their competency in information literacy after continuous efforts in course curriculum. Data were gathered in 2015–2016 from first year interdisciplinary project-based courses with STEM students to assess changes in information literacy competencies. Analyses suggested there was a significant increase in students' perception of their familiarity with library resources, search strategies, citation use, and ability to evaluate source quality. There was no change in their perception of their ability to evaluate the variety of source and source relevance and a decrease in their perception of the ethical use of information. With faculty-librarian partnership and integrated instruction in a first year course, students showed the largest perceived increases in foundational information literacy skills but struggled with information literacy abilities. This study provides a baseline of information literacy competencies of first year students and provides recommendations for continuing education.     

Interdisciplinary scholarly collaboration in data-intensive,public-funded,international digital humanities project work

The information practices of collaborative interdisciplinary researchers, especially in distributed environments, remain understudied. Embracing the naturalistic paradigm, this qualitative case study relies upon semi-structured interviews, snowball sampling, and grounded analysis. Based on interviews with 53 participants from 11 Digging Into Data 3 (DID3, 2014-2016) projects, this study examines the working practices of scholars (library and information science professionals, humanists, and computer scientists) engaging in collaborative, international, data-intensive, publicly-funded interdisciplinary research. Benefits of such work include avoiding redundancy, exploding disciplinary silos, and more ambitious, larger-scale outputs. Challenges and lessons learned center on innovation, flexibility, and failure, translation, intermediaries, divisions of labor and delegation of responsibilities and tasks, conflict resolution, technology, outputs and publication, and timeframe, sustainability, and infrastructure.     

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.