Unveiling the Predators: Contemporary Approaches to Identifying Illegitimate Open Access Journals in the Academic Publishing Ecosystem

Predatory journals undermine the integrity of the Open Access publishing model by profiting from its structure while neglecting critical editorial and peer-review processes. Current identification methods, from manual blacklists to machine learning, suffer from limitations including definitional ambiguity, reliance on binary classifications, and issues with scalability, reliability, and interpretability. A new methodology is proposed to address these systemic shortcomings.

Why it matters

The proliferation of predatory journals poses a significant risk to the credibility and trustworthiness of published research, impacting decision-making based on potentially unverified information across all sectors. Addressing this challenge is crucial for maintaining standards in academic discourse and ensuring the integrity of the knowledge base that informs strategic initiatives.

Key insights

• Predatory journals exploit the Open Access model for financial gain, bypassing essential editorial and peer-review standards.
• Existing methodologies for identifying predatory journals include manual blacklist checks and advanced automated approaches using machine learning.
• Current identification methods are hindered by the lack of a universally accepted definition for predatory journals.
• There is an over-reliance on binary classification systems (e.g., blacklists and whitelists) in current identification efforts.
• Existing approaches face issues related to scalability, reliability, and interpretability.
• A novel methodology is introduced to address the identified shortcomings in predatory journal identification.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.10739