Journal of Academic Librarianship最新文献

Providing and receiving feedback are crucial elements of any learning exchange and are an intrinsic part of cultivating disciplinary dialogic culture. While there is considerable scholarly conversation on the topic of cultivating constructive feedback in student work, there is less literature dedicated to addressing what successful feedback literacy and processes look like in relation to scholarly research writing by professionals. This literature review examines the development of feedback literacy concepts across disciplines to determine if and how these concepts manifest within academic librarianship literature. The goal of this paper is to gain insights to improve future feedback exchange within the profession. Results indicate that the concept of feedback literacy is still largely under-explored in library and information science circles. Suggestions are made for improvements to address this gap.

In recent years, academic libraries have been under increasing pressure to embrace the winds of change in the face of new trends, scenarios, and uncertainty to more effectively fulfill the unchanging mission of information delivery. As a core component of the university ecosystem, academic libraries actively explore innovative approaches to generating and disseminating information to their users. However, many academic libraries in African universities are slow to transform and thereby encounter challenges in their quest to shape university education. Using a comparative approach, this study looks at three stages of global revolutions (pre-COVID-19, post-COVID-19, and the current GenAI era) that have prompted universities in Africa to adapt, stay relevant, and meet educational goals. A systematic review was conducted on the Web of Science (WoS) and the Scopus database to investigate the innovative trajectory of academic libraries in Africa across the three stages. Out of 340 articles retrieved, a total of 111 articles were selected for analysis. The findings suggest that educators in numerous African universities employed innovative methods (e.g., Web 2.0 applications, digital databases and repositories, open distant libraries, mobile websites, and professional development) to transform academic libraries at all three stages. The shift from traditional library systems to more dynamic, digitized platforms came with challenges such as poor internet access, lack of technological skills and infrastructure, insufficient funding, and poor digitization policies. Moreover, Africa is still limited in terms of generative AI technology-integrated library services. To improve university education, academic libraries should utilize existing and emerging technologies to innovate their services.

This paper reports an investigation into the perception of academic librarians on data competency in their daily roles across various library departments in the United States and Canada. Through a survey, we sought to uncover the scope of data-related tasks librarians are engaged in, the tools they use, their aspirations for professional development in data competencies, and their expectations from Library and Information Science (LIS) education programs. The findings reveal a complex engagement pattern with data tasks, with librarians in data-specific roles dedicating a considerable portion of their work to these activities, while the majority engage less frequently, indicating that data tasks are a minor part of their overall responsibilities. Despite a limited frequency of data visualization in their current roles, there is a pronounced interest among librarians to enhance skills in this area. Our study identifies a crucial need for improved competencies in data management and collection development, especially in roles related to cataloging, library systems, and special collections. Additionally, our findings reveal a critical gap between academic libraries' demand for data skills and the content coverage in MLIS programs, emphasizing the need for curriculum updates to prepare librarians for the evolving information landscape.

Generative artificial intelligence (AI) and large language models (LLMs) have induced a mixture of excitement and panic among educators. However, there is a lack of consensus over how much experience science and engineering students have with using these tools for research-related tasks. Likewise, it is not yet known how educators and information professionals can leverage these tools to teach students strategies for information retrieval and knowledge synthesis. This study assesses the extent of students' use of AI tools in research-related tasks and if information literacy instruction could impact their perception of these tools. Responses to Likert-scale questions indicate that many students did not have extensive experience using LLMs for research-related purposes prior to the information literacy sessions. However, after participating in a didactic lecture and discussion with an engineering librarian that explored how to use these tools effectively and responsibly, many students reported viewing these tools as potentially useful for future assignments. Student responses to open-response questions suggest that librarian-led information literacy training can assist students in developing more sophisticated understandings of the limitations and use cases for artificial intelligence in inquiry-based coursework.

According to Scopus, China is the nation that produces the highest volume of scientific research but is also the nation with the highest number of retractions, suggesting there are issues connected to research and publishing ethics within the Chinese publishing infrastructure. One source of negative reputation may be the selection of journals with questionable reputation, including “predatory” journals. In 2020, the Center of Scientometrics (CoS) in China established a list of “problematic” journals, called the Chinese Early Warning Journal List (EWJL), the only national watchlist in China, to support Chinese academics and the Ministry of Science and Technology of China. EWJL ranks journals as either low, medium or high risk. There are benefits if EWJL is accurate, valid and complete. However, close examination of the CoS website and EWJL functionality revealed several deficiencies. This paper debates those weaknesses within the wider context of publication in low-quality journals, offering suggestions for improvement that would be necessary for EWJL to become more trustworthy, and to better enable the continual reform of Chinese publishing culture. This issue is important to academic librarians because they can use EWJL in the process of collecting library funds and providing library information, and advice, to researchers.

The factors contributing to different levels of artificial intelligence (AI) adoption by librarians and their patrons need clarifying in the context of literature reviews. This paper addresses this need by exploring the transformative impact of AI on literature reviews, particularly within academic librarianship in the health sciences. Drawing on literature and professional experience, it examines how AI is reshaping reviews, potentially extending their meaning beyond text-based sources to accommodate multimedia content and predictive insights. While highlighting AI's promise in enhancing research efficiency and comprehensiveness, the paper also notes the lack of documentation of AI's uptake for literature reviews, perhaps reflecting concerns over reliability and biases. Proposed strategies for moving forward include matching different literature reviews with the most appropriate AI systems. This alignment guides librarians and researchers in navigating the complexities of AI adoption, using human oversight to ensure the integrity and quality of AI content. The paper underscores the importance of education, training, and continuous consultation to promote trustworthy and responsible AI utilization. This pathway foresees more robust outcomes from literature reviews in domains like health care in the digital age.

The digital era has fostered significant advancements in data visualization (DV) within the field of academic libraries, establishing it as a focal point of widespread interest. The objectives of this study are: i) to identify the responsibilities that professionals working in the DV field are expected to undertake, and ii) to analyze the current stated qualifications and competencies required for DV-related positions. This study adopted content analysis approach by extracting and organizing DV-related job posting data from the IASSIST Jobs Repository and the IFLA LIBJOBS website through NCapture and NVivo tools, and using VOSviewer and Pajek software to conduct frequency and thematic clustering analyses of job characteristics, responsibilities and competency requirements of 126 job advertisements identified in the final screening. The findings indicated that library professionals in the field of DV are increasingly tasked with a broader spectrum of responsibilities and duties, with a pronounced preference for those demonstrating expertise in cross-disciplinary domains and possessing exceptional general competencies, in addition to the requisite professional qualifications and skills, such as interdisciplinary liaison and commitment to equity and diversity. This study offers training and professional development direction for academic librarians and prospective practitioners, as well as practical recommendations for the transformation of library services.

This article addresses the question of whether personal publication lists should be used as a data source in research evaluation, or whether, as is widespread in practice, existing databases, such as Web of Science, can be used instead. For this purpose, an empirical study was carried out in which all business administration university professors (n = 233) of a non-English-speaking country, namely Austria, were ranked in several ways (e.g., full or fractional counting, consideration or non-consideration of journal rankings). All rankings were based on the number of published journal articles (n = 4246; observation period: 10 years). In one case, the personal publication lists and in the other case, the Web of Science were used as data source for these rankings. The rankings created in these two ways were compared with each other. The results show that the choice of the data source has a major influence on the ranking results. For researchers from non-English-speaking countries with (many) publications in their respective national languages, an exclusive use of international databases, such as Web of Science in our case, cannot fully consider the whole research performance. In these cases, the use of personal publication lists seems to make a lot of sense, at least for several ranking variants, despite the effort involved. The main contribution of our study is that we compare personal publication lists as a data source with Web of Science which is often used in research evaluations. In addition, this comparison is not, as usual, input-related (based on the degree of coverage in the two data sources) but impact-related (based on rankings that are created based on the publications contained in the two data sources).

Faculty status for librarians is one of the most debated topics within academic librarianship (Bailey & Becher, 2022). Professional associations argue that faculty status benefits librarians, but little empirical work has explored how faculty status plays out in real-world settings (Galbraith, Garrison, & Hales, 2016). This study investigates whether faculty status helps librarians overcome barriers using a comparative case study of two academic libraries representing polarity on this issue. One library offers librarians tenure-track faculty appointments; the other classifies librarians as staff. Through qualitative analysis of semi-structured interviews, the study explores administrative obstacles faced by librarians and whether faculty status ameliorates them. This study also examines if debates over librarian faculty status ignore inequality regimes (Acker, 2006) that may appear in both faculty and staff settings. Surprisingly, findings reveal faculty status is associated with more workplace hierarchies and stress, especially for female librarians. Contrary to claims made by faculty status proponents, most librarians feel equally undervalued by disciplinary faculty regardless of status. Ultimately, blanket recommendations for faculty status seem ineffective, and nuanced solutions tailored to local contexts better serve librarians. This study encourages an honest dialogue to empower librarians based on individual needs, not rigid assumptions.

This study uses data for >3300 business and economics journals to explore the relationships between 5 subjective (expert) journal ratings and 10 citation metrics including 5IF (5-year Impact Factor), Article Influence (AI) score, CiteScore, Eigenfactor, Impact per Publication, SJR, and SNIP. Overall, AI and SJR are the citation metrics most closely related to the expert journal ratings. Comparisons of paired citation metrics that are similar in all but a single key characteristic confirm that expert journal ratings are more closely related to size-independent citation metrics than to size-dependent metrics, more closely related to weighted metrics than to unweighted metrics, and more closely related to normalized metrics than to non-normalized metrics. These results, which are consistent across the 5 expert ratings, suggest that evaluators consider the average impact of an article in each journal rather than the total impact of the journal as a whole, that they give more credit for citations in high-impact journals than for citations in lesser journals, and that they assess each journal's relative standing within its own field or subfield rather than its broader scholarly impact. No single citation metric is a good substitute for any of the expert ratings considered here.