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Session Overview
Session
Paper Session 08: Scientometrics and Bibliometrics
Time:
Sunday, 31/Oct/2021:
4:00pm - 5:30pm

Session Chair: Ly Dinh, University of Illinois at Urbana-Champaign, USA
Location: Salon I, Lobby Level, Marriott


External Resource:
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Presentations
4:00pm - 4:15pm
ID: 101 / PS-08: 1
Short Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Data Science; Analytics; and Visualization
Keywords: Bibliometrics, Library and Information Science, Scholarly communication, Scientific communities

Journals as Communities: A Case Study of Core Journals in LIS

Jeppe Nicolaisen1, Tove Faber Frandsen2

1University of Copenhagen, Denmark; 2University of Southern Denmark, Denmark

This paper proposes an indicator for measuring the level of commitment to academic journals. The indicator is demonstrated on a sample of core LIS-journals. By monitoring authorship patterns over a 20-year period, it is shown that some journals have a higher frequency of returning authors than others, consequently showing a larger degree of community commitment. The paper discusses how the indicator may be applied when studying factors influencing researchers’ journal selection decisions.



4:15pm - 4:45pm
ID: 118 / PS-08: 2
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Data Science; Analytics; and Visualization
Keywords: Information Literacy Competency Standards for Higher Education; Framework for Information Literacy for Higher Education; Information Literacy, Higher education; Bibliometric mapping analysis

Research Trends from a Decade (2011-2020) for Information Literacy in Higher Education: Content and Bibliometric Mapping Analysis

Chao-Chen Chen1, Ning-Chiao Wang2, Yun-Fang Tu3, Hsin Ju Lin4

1Chung Yuan Christian University and National Taiwan Normal University, Taiwan; 2University of Wisconsin-Milwaukee, USA; 3Fu Jen Catholic University, Taiwan; 4National Taiwan Normal University, Taiwan

New terms and theoretical concepts in information literacy have emerged over the last decade, and these have led to revisions in the standards for information literacy. In order to determine whether information literacy research has reflected these trends, we collected SSCI literature for the 2011 to 2020 period related to information literacy in higher education (ILHE) and conducted analysis using bibliographic mapping and content analysis. Our research found that the volume of research on ILHE has increased in the last five years as compared to the five years before that, and that keywords related to literacy (such as “digital literacy” and “multiliteracies”) have been getting a great deal of discussion. After the Framework for Information Literacy for Higher Education (FILHE) was announced, curriculum design research based on the Information Literacy Competency Standards for Higher Education (ILCSHE) continued to outnumber that done based on the Framework.



4:45pm - 5:00pm
ID: 230 / PS-08: 3
Short Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Library and Information Science
Keywords: Open Access; Bibliometrics analysis; Research impact; Self-citation; Covid-19;

Use of Bibliometrics Data to Understand the Citation Advantages of Different Open Access Categories in Covid-19 Related Studies

Xiaoju Julie Chen, Neelam Bharti, Matthew Marsteller

Carnegie Mellon University, USA

The number of Open Access (OA) research articles is trending upward. This research aims to understand the correlations between different OA types and the impact of OA research articles evaluated based on the citation numbers. To avoid bias caused by the publication year, we chose to use COVID-19 studies in different fields to take advantage of this topic’s quick turnaround of data. We analyzed the bibliometrics data and citation numbers (excluding self-citations) of around 42,000 English language articles published in 2020 related to COVID-19. We evaluated different types of OA categories such as Gold, Bronze, and Hybrid articles separately. Results show that amongst all OA categories, Hybrid/Green and Bronze/Green OA articles had significant citation advantages. Green OA articles returned more citations than articles with the other OA status. Gold OA articles have no citation advantages compared to non-OA articles. Gold/Green OA articles had the highest self-citation rates, followed by Non-OA articles. The results of the study can be used in understanding different OA categories and the reasons for OA choices. Certain strategies can be made accordingly to improve the awareness of OA in different fields and help OA publishers to improve the OA services.



5:00pm - 5:30pm
ID: 240 / PS-08: 4
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Library and Information Science
Keywords: citations, scientometrics, citation motives, influence, scholarly publishing

Citation Quantity Increases Citation Quality

Misha Teplitskiy1, Eamon Duede2, Michael Menietti3, Karim Lakhani3

1University of Michigan, USA; 2University of Chicago, USA; 3Harvard University, USA

Scholars typically measure the influence of scientific work using citation counts, but many citations are symbolic and denote little-to-no influence. A common view is that highly cited papers may be especially appealing for “persuasion by name-dropping” and attract many symbolic citations, making their citations denote less influence on average. Here, we rigorously test this view using customized author surveys about the intellectual influence of referenced work on an author’s own papers, collecting data on 17,154 referencing decisions from 9,380 corresponding authors. Results are contrary to persuasion by name-dropping: while most citations (54%) had little influence on their citers, citations to the most highly cited papers were two to three times more likely to denote high influence. To explain this pattern we develop a process model based on status signals, and support it with experimental and associational data. Overall, we find that authors invest more attention into highly cited papers and cite them less symbolically, making these papers influence the research frontier even more than their raw citation counts suggest.



 
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