Analysis Of Acknowledgments of Libraries in the Journal Literature Using Machine Learning. Issue 1 (14th October 2022) - Hubbard, David; Laddusaw, Sierra; Tan, Qiaoyu; Hu, Xia
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.