Analysis Of Acknowledgments of Libraries in the Journal Literature Using Machine Learning. Issue 1 (14th October 2022)

• Record Type: Journal Article
• Authors: Hubbard, David; Laddusaw, Sierra; Tan, Qiaoyu; Hu, Xia
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 59:Issue 1(2022)
• Page Start: 709
• Page End: 711
• Publication Date: 2022-10-14
• Subjects: Acknowledgment -- Bibliometric analysis -- Libraries -- Machine Learning -- Web of Science; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.698
• Languages: English
• ISSNs: 2373-9231

Abstract

ABSTRACT: Increasing emphasis is being placed on research impact and it has prompted scholars to explore contributions beyond traditional research impact metrics. Acknowledgments, which are formal statements of indebtedness and contribution, within the journal literature provide an additional means to assess impact. This study examines contributions of libraries to the scholarly literature within acknowledgments using a combination of machine learning and manual methods to quantify and characterize acknowledgments.