Learned societies in the academic landscape: Challenges in identifying and categorizing organizations. Kulczycki, E., Pölönen, J., Laakso, M., & Taskin, Z. November, 2024. ![Learned societies in the academic landscape: Challenges in identifying and categorizing organizations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This study explores ways of identifying learned societies within the Research Organization Registry (ROR), a type of organization that so far lacks its own category within the registry. Despite their significant role in the academic landscape, learned societies lack distinct recognition in global databases, which lowers their visibility and perceived impact. Using enhanced ISSN data and the existing ROR database, we present a methodology to identify learned societies based on their publishing activities and organizational names. Our approach is validated against national lists of societies from Austria, Finland, and the UK, demonstrating the feasibility and reliability of our method which, however, has several limitations discussed in the paper. Our findings show that 92% of 1,471 societies identified in ROR are currently assigned to ‘Other’ and ‘Nonprofit’ categories. We also highlight the geographical distribution and field-specific categorization of learned societies, emphasizing the diversity and scope of their influence. This paper contributes to the science of science field by proposing a framework that enhances the visibility and recognition of learned societies globally. Our research argues that ROR should consider enhancing their data schema to accommodate for learned societies. This way ROR can provide an open research information resource for identifying learned societies both for research and practical purposes.
@misc{kulczycki_learned_2024,
title = {Learned societies in the academic landscape: {Challenges} in identifying and categorizing organizations},
shorttitle = {Learned societies in the academic landscape},
url = {https://osf.io/3zgks},
doi = {10.31235/osf.io/3zgks},
abstract = {This study explores ways of identifying learned societies within the Research Organization Registry (ROR), a type of organization that so far lacks its own category within the registry. Despite their significant role in the academic landscape, learned societies lack distinct recognition in global databases, which lowers their visibility and perceived impact. Using enhanced ISSN data and the existing ROR database, we present a methodology to identify learned societies based on their publishing activities and organizational names. Our approach is validated against national lists of societies from Austria, Finland, and the UK, demonstrating the feasibility and reliability of our method which, however, has several limitations discussed in the paper. Our findings show that 92\% of 1,471 societies identified in ROR are currently assigned to ‘Other’ and ‘Nonprofit’ categories. We also highlight the geographical distribution and field-specific categorization of learned societies, emphasizing the diversity and scope of their influence. This paper contributes to the science of science field by proposing a framework that enhances the visibility and recognition of learned societies globally. Our research argues that ROR should consider enhancing their data schema to accommodate for learned societies. This way ROR can provide an open research information resource for identifying learned societies both for research and practical purposes.},
language = {en-us},
urldate = {2024-11-22},
publisher = {OSF},
author = {Kulczycki, Emanuel and Pölönen, Janne and Laakso, Mikael and Taskin, Zehra},
month = nov,
year = {2024},
keywords = {ISSN, Research Organization Registry, learned societies, research organizations},
}
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