Trends and Growth Modelling of Research Output in the Journal of Genetics: A Bibliometric Perspective

Abstract

This study presents a bibliometric analysis of publication trends and growth modelling of research output published in the Journal of Genetics over fifty years 1974 to 2024. Data comprising 2,105 records were retrieved from the Web of Science Core Collection and analysed using quantitative bibliometric techniques. Growth indicators such as Annual Average Growth Rate (AAGR), Compound Annual Growth Rate (CAGR), Relative Growth Rate (RGR), and Doubling Time (DT) were employed to examine the dynamics of scholarly output. The findings reveal distinct phases of development, including an initial period of sporadic growth, phases of rapid expansion, consolidation, peak productivity, and a recent slowdown in growth intensity. To further understand long-term publication behaviour, multiple growth models, linear, exponential, logarithmic, quadratic, and logistic, were applied. Among these, the logistic model provided the best fit, indicating that the journal’s publication growth follows a mature and stabilised trajectory. The study highlights the usefulness of growth modelling in journal-level bibliometric analysis and offers insights into the evolution of genetics research dissemination.