Dangzhi Zhao

Purpose: To add new empirical knowledge to debates about social practices of peer production communities, and to conversations about bias and its implications for democracy. To help identify Wikipedia (WP) articles that are affected by systematic bias and hopefully help alleviate the impact of such bias on the general public, thus helping enhance both traditional (e.g. libraries) and online information services (e.g. Google) in ways that contribute to democracy. This paper aims to discuss the aforementioned objectives.Design/methodology/approach: Quantitatively, the authors identify edit-warring camps across many conflict zones of the English language WP, and profile and compare success rates and typologies of camp edits in the corresponding topic areas. Qualitatively, the authors analyze the edit war between two senior WP editors that resulted in imbalanced and biased articles throughout a topic area for such editorial characteristics through a close critical reading.Findings: Through a large-scale quantitative study, the authors find that winner-take-all camps exhibit biasing editing behaviors to a much larger extent than the camps they successfully edit-war against, confirming findings of prior small-scale qualitative studies. The authors also confirm the employment of these behaviors and identify other behaviors in the successful silencing of traditional medicinal knowledge on WP by a scientism-biased senior WP editor through close reading.Social implications: WP sadly does, as previously claimed, appear to be a platform that represents the biased viewpoints of its most stridently opinionated Western white male editors, and routinely misrepresents scholarly work and scientific consensus, the authors find. WP is therefore in dire need of scholarly oversight and decolonization.Originality/value: The authors independently verify findings from prior personal accounts of highly power-imbalanced fights of scholars against senior editors on WP through a third-party close reading of a much more power balanced edit war between senior WP editors. The authors confirm that these findings generalize well to edit wars across WP, through a large scale quantitative analysis of unbalanced edit wars across a wide range of zones of contention on WP.

Purpose: This study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011–2020. It also examines changes in this structure from 2006–2010 through 2011–2015 to 2016–2020. Results will contribute to a better understanding of the information science research field.Design/methodology/approach: The well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011–2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor.Findings: The two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011–2015 but shared dominance with information behavior during 2016–2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016–2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011–2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016–2020.Originality/value: Author co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.

Purpose: Wikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data.Design/methodology/approach: We adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics.Findings: Seven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not.Originality/value: Given the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.

The present study examines the intellectual structure of research on coronavirus, as revealed from an author co‐citation analysis using citation data retrieved from the Web of Science Core Collection and mapped to the PubMed database. Four major dimensions are identified: I) outbreaks, II) viral structure and function, III) vaccine and therapeutic development, and IV) coronaviruses found in a range of animals. The “outbreaks” dimension is by far the most prominent, dominated by reports on the three recent major outbreaks: COVID-19, severe acute respiratory syndrome, and Middle East respiratory syndrome. The focus of research on major outbreaks is on public health and clinical research, with focus on disease characterization, diagnosis, transmission, and clinical course. Notably, certain clinically important areas, such as mental health during outbreaks and viral surveillance, among others, did not stand out as identifiable specialties or topics in the coronavirus research landscape. Results from this study should contribute to the understanding of the coronavirus research landscape and to the identification of strengths and weaknesses of current research on COVID-19.

Purpose: This paper aims to identify patterns, trends and potential implications related to post-checkout non-usage (material that is checked out by a user, but subsequently never opened and/or downloaded) of library digital content. Design/methodology/approach: A large urban Canadian public library’s data (2013-2017) from Rakuten OverDrive was analyzed. Pending items (items that are checked out, but neither opened nor downloaded) were compared with total checkouts to determine post-checkout non-usage rates. Findings: Checkouts and overall rates of post-checkout non-usage of e-books and e-audiobooks have risen significantly and consistently. Juvenile and non-fiction e-books demonstrate higher post-checkout non-usage rates than adult and fiction e-books, respectively. The library spends up to US$10,700 per year on metered access e-books that are never opened by users. This number has grown significantly over the years. Originality/value: E-materials in libraries have been growing rapidly, but their current lending models are still largely a direct application of concepts in traditional library services that have developed based on physical materials, such as checkouts, due dates, renewals, holds and wait times. However, e-materials do not have the limitation of physical materials that prevents other users from accessing a checked-out item, which makes many of the traditional concepts no longer applicable. New concepts and lending models should be developed that allow users to access any library e-materials at any time, and are financially functional and sustainable for both libraries and e-content providers.

Author self‐citations were examined as to their function, frequency, and location in the full text of research articles and compared with external citations. Function analysis was based on manual coding of a small dataset in the field of library and information studies, whereas the analyses by frequency and location used both this small dataset and a large dataset from PubMed Central. Strong evidence was found that self‐citations appear more likely to serve as substantial citations in a text than do external citations. This finding challenges previous studies that assumed that self‐citations should be discounted or even removed and suggests that self‐citations should be given more weight in citation analysis, if anything.

The present study analyzed a large urban Canadian public library's data (2013‐2017) from Rakuten OverDrive to identify patterns, trends, and potential implications related to post‐checkout non‐usage (material that is checked out by a user, but subsequently never opened and/or downloaded) of library digital content. It was found that over 12% of the more than 1.1 million checkouts of digital content via OverDrive in 2017 were never opened, causing a waste of over $10,000 USD on metered access eBooks alone; this figure will likely increase in the coming years based on the trends found in this study. Juvenile and non‐fiction eBooks are most likely to be checked out and go unused. These findings may shed light on ways libraries and digital content vendors might improve the efficiency of digital content lending, and serve to inform collection management and user‐targeted marketing and solutions.

Purpose: (1) To test basic assumptions underlying frequency-weighted citation analysis: (a) Uni-citations correspond to citations that are nonessential to the citing papers; (b) The influence of a cited paper on the citing paper increases with the frequency with which it is cited in the citing paper. (2) To explore the degree to which citation location may be used to help identify nonessential citations. Design/methodology/approach: Each of the in-text citations in all research articles published in Issue 1 of the Journal of the Association for Information Science and Technology ( JASIST ) 2016 was manually classified into one of these five categories: Applied, Contrastive, Supportive, Reviewed, and Perfunctory. The distributions of citations at different in-text frequencies and in different locations in the text by these functions were analyzed. Findings: Filtering out nonessential citations before assigning weight is important for frequency-weighted citation analysis. For this purpose, removing citations by location is more effective than re-citation analysis that simply removes uni-citations. Removing all citation occurrences in the Background and Literature Review sections and uni-citations in the Introduction section appears to provide a good balance between filtration and error rates. Research limitations: This case study suffers from the limitation of scalability and generalizability. We took careful measures to reduce the impact of other limitations of the data collection approach used. Relying on the researcher’s judgment to attribute citation functions, this approach is unobtrusive but speculative, and can suffer from a low degree of confidence, thus creating reliability concerns. Practical implications: Weighted citation analysis promises to improve citation analysis for research evaluation, knowledge network analysis, knowledge representation, and information retrieval. The present study showed the importance of filtering out nonessential citations before assigning weight in a weighted citation analysis, which may be a significant step forward to realizing these promises. Originality/value: Weighted citation analysis has long been proposed as a theoretical solution to the problem of citation analysis that treats all citations equally, and has attracted increasing research interest in recent years. The present study showed, for the first time, the importance of filtering out nonessential citations in weighted citation analysis, pointing research in this area in a new direction.