From Warrant to Forecast: Rethinking Scholarly Classification with ‘Data Science'

Authors

DOI:

https://doi.org/10.17821/srels/2025/v62i6/171859

Keywords:

Data Science, Exponential Models, Libraries, Literature Growth, Literary Warrant, Predictive Modelling, Time Series Forecasting

Abstract

This paper incorporates the Time Series Forecasting Method to Libraries as a means of choosing documents for users and to examine the growth pattern that supports Literary Warrant as well as User Warrant. Based on a study of the growth of literature in Data Science, the study suggests that a data-driven approach ensures libraries remain relevant and responsive in a constantly evolving information landscape. It argues that literary warrant serves as a useful principle for library services, and when applied, coupled with forecasting techniques, empowers librarians to anticipate user needs.

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Published

2025-12-31

How to Cite

Roy, A., & Ghosh, S. (2025). From Warrant to Forecast: Rethinking Scholarly Classification with ‘Data Science’. Journal of Information and Knowledge, 62(6), 365–379. https://doi.org/10.17821/srels/2025/v62i6/171859

Issue

Volume 62, Issue 6, December 2025

Section

Articles

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Copyright (c) 2025 Journal of Information and Knowledge

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All the articles published in Journal of Information and Knowledge are held by the Publisher. Sarada Ranganathan Endowment for Library Science (SRELS), as a publisher requires its authors to transfer the copyright prior to publication. This will permit SRELS to reproduce, publish, distribute and archive the article in print and electronic form and also to defend against any improper use of the article.

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