DEVELOPMENT AND PILOT EVALUATION OF DPJSS: A MAQASID-BASED DATA-DRIVEN PREDATORY JOURNAL SCREENING SYSTEM FOR ETHICAL SCHOLARLY PUBLISHING

Abstract

The rapid growth of academic publishing has intensified concerns regarding predatory journals that exploit researchers through misleading indexing claims, weak peer review processes, aggressive publication invitations and non-transparent publishing practices. These practices threaten research integrity and reduce the quality of scholarly communication. This study aimed to develop the Data-Driven Predatory Journal Screening System (DPJSS) as a data-driven and ethically guided decision-support prototype to assist researchers in identifying journal selection risks. A prototype development approach was employed involving indicator construction, dataset development, system configuration, expert validation and pilot user evaluation. The prototype dataset consisted of 12 journal samples equally distributed across three categories: Legitimate, Questionable and Suspected Predatory. The system was developed using Google Forms, Google Sheets and Looker Studio, integrating automated scoring, risk classification and dashboard visualisation. In addition, the study introduced an AI-assisted evidence extraction mechanism to support journal information extraction without generating automated decisions. DPJSS also integrated Maqasid al-Shariah principles, particularly Hifz al-‘Aql and Hifz al-Mal, as an ethical filtering layer. The findings demonstrated that DPJSS successfully differentiated journal risk levels through structured scoring and achieved positive user acceptance during pilot evaluation (M = 4.27; α = 0.916). This study contributes to the development of a more systematic, transparent and integrity-oriented journal screening system to strengthen scholarly publishing governance.