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Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
Paper Session 17: Science of Science
Time:
Tuesday, 02/Nov/2021:
9:00am - 10:30am

Session Chair: Chris Cunningham, North Carolina Central University, USA
Location: Salon C, Lobby Level, Marriott

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Presentations
9:00am - 9:15am
ID: 164 / PS-17: 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: Library and Information Science
Keywords: Interdisciplinarity; Disparity; Node2Vec; Citation analysis; Scholarly communication

Measurement of Interdisciplinarity: Quantifying Distance-Based Disparity Using Node2vec

Hongyu Zhou, Raf Guns, Tim Engels

University of Antwerp, Belgium

When quantifying the level of interdisciplinarity for scientific research, most established indicators employ a three-element diversity framework, namely variety, balance, and disparity, each of which captures a distinct but insufficient element. Among three, disparity, i.e. how different (dissimilar) the categories within a system are, is the most challenging one due to its calculation cost and conceptual ambiguity. The discriminative power for disparity is found to be weakened in more fine-grained science classification schemes. To address this issue, this paper proposes a new method for quantifying disparity by applying Node2vec on the discipline citation network and retrieving distance between disciplines using embeddings vectors. Compared to cosine-based dissimilarity for disparity, our proposed method exhibited broader distribution and less skewness for disparity values, which could potentially lead to higher discriminative power of interdisciplinarity. A case study for Linguistics is also conducted to show the capability of detecting variations in disparity of the proposed method.



9:15am - 9:45am
ID: 277 / PS-17: 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: Library and Information Science
Keywords: Interdisciplinary collaboration, Disruption, Regression analysis

Is Interdisciplinary Collaboration Research More Disruptive Than Monodisciplinary Research?

Xin Liu1, Yi Bu2, Ming Li1, Jiang Li1

1Nanjing University, People's Republic of China; 2Peking University, People's Republic of China

As an important pattern of scientific research, interdisciplinary collaboration is universally encouraged by science and technology policy makers. However, it remains a question whether interdisciplinary collaboration research is more disruptive than monodisciplinary research. To address this research question in this study, interdisciplinary collaboration is measured as whether the authors of a paper are from at least two disciplines, and the degree of "disruptive" is measured by the Disruption index proposed by Funk & Owen-Smith (2017). By using articles published in six journals from 1978 to 2019 in the Microsoft Academic Graph (MAG) database, we constructed an OLS regression model with journal fixed effect and time fixed effect to analyze the influence of interdisciplinary collaboration on the Disruption values with different citation windows. The findings show that interdisciplinary collaboration research is less disruptive than monodisciplinary research.



9:45am - 10:00am
ID: 217 / PS-17: 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: Research Lineage, Citation Significance Detection, Feature Engineering, Machine Learning, Idea Propagation

A Step Towards Finding a Research Lineage Leveraging on Identification of Significant Citations

Tirthankar Ghosal, Muskaan Singh

Charles University, Czech Republic

Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant prior work and causes researchers to spend a considerable amount of time building up their literature list. Citations play a significant role in discovering relevant literature. However, not all citations are created equal. A majority of the citations that a paper receives are for providing contextual, and background information to the citing papers and are not central to the theme of those papers. However, some papers are pivotal to the citing paper and inspire or stem up the research in the citing paper. Hence the nature of citation the former receives from the later is significant. In this work in progress paper, we discuss our preliminary idea towards establishing a lineage for a given research via identifying significant citations. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for at least a certain category of papers. The distal goal of this work is to identify the real impact of research work or a facility beyond direct citation counts.



10:00am - 10:30am
ID: 264 / PS-17: 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: double-blind peer review, prestige bias, choice architecture

Does Double-Blind Peer Review Reduce Bias? Evidence from a Top Computer Science Conference

Mengyi Sun, Jainabou Dafna, Misha Teplitskiy

University of Michigan, USA

Peer review is essential for advancing scientific research, but there are long-standing concerns that reviewers are biased by authors' prestige or other characteristics. Double-blind peer review has been proposed as a way to reduce reviewer bias, but the evidence for its effectiveness is limited and mixed. Here, we examine the effects of double-blind peer review by analyzing the peer review files of 5027 papers submitted to a top computer science conference that changed its reviewing format from single- to double-blind in 2018. We find that after switching to double-blind review, the scores given to the most prestigious authors significantly decreased. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance significantly. The inter-reviewer disagreement increased significantly in the double-blind format. Papers rejected in the single-blind format are cited more than those rejected under double-blind, suggesting that double-blind review better excludes poorer quality papers. Lastly, an apparently unrelated change in the rating scale from 10 to 4 points likely reduced prestige bias significantly such that papers’ acceptance was affected. These results support the effectiveness of double-blind review in reducing biases, while opening new research directions on the impact of peer review formats.



 
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