A Framework for Pre-Pages Generator and Visualiser for Journal Publishing Systems: A Case Study
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Abstract
In the domain of academic publishing, publishers must establish an efficient editorial workflow for managing their publication schedule while producing quality content, as these two processes will ultimately provide a good experience to readers. Many independent publishers utilizing Open Journal Systems (OJS) rely on a manual compilation of "pre-pages" (front matter), including the Issue Cover, the Editorial Board List, and the Table of Contents, through a manual workflow that is labor-intensive and highly prone to human error, resulting in a product that is inconsistently formatted. Through this project, we have developed a web-based Pre-Pages Generator and Visualiser that automates the pre-publishing process, developed using Python and the Streamlit framework. The application utilizes a 5-step Wizard workflow to accomplish the entire pre-publishing process by integrating web scraping to extract existing editorial data from the target journal's website, cleansing the data, and leveraging template-driven rendering to populate Word templates with the appropriate OJS submission data. In addition, the Editorial Intelligence Dashboard provides an overview of issue composition and editorial insights into content trends across issues of the publication by transforming raw bibliometric data. A prototype of this system was developed in Python and was evaluated using the metadata of the Journal of Informatics and Web Engineering (JIWE) as a validation case study. Empirical output shows an estimated 80% time reduction in document preparation (reducing hours of manual labor to minutes) and a decrease in formatting inconsistencies. The findings suggest that this prototype could help to increase operational efficiency, reduce metadata risk, and improve decision support for non-technical editorial teams.
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