揭露掠夺者：学术出版生态系统中识别非法开放获取期刊的当代方法
Unveiling the Predators: Contemporary Approaches to Identifying Illegitimate Open Access Journals in the Academic Publishing Ecosystem
AI总结：
本文针对掠夺性非法OA期刊识别方法的局限性，提出基于多元图分析的新框架，可提升检测的准确性、可扩展性与透明度，助力理解学术出版格局。
AI中文摘要：
掠夺性期刊通过利用开放获取（OA）出版框架谋取经济利益，同时规避必要的编辑和同行评审标准，对开放获取出版模式的完整性构成重大挑战。本研究批判性评估了识别此类期刊的现有方法，范围从手动黑名单检查到利用机器学习的高级自动化方法。分析凸显了关键局限性，包括缺乏普遍接受的掠夺性期刊定义、过度依赖二元分类系统（例如黑名单和白名单），以及可扩展性、可靠性和可解释性方面的问题。为解决这些不足，本文引入了一种基于多元图分析的新方法。通过将学术出版生态系统建模为作者、文章、期刊和出版商等相互关联实体的网络，该方法能更广泛地洞察学术交流的动态，可借助中心性度量、社区检测和异常检测等图算法识别非法出版实践。所提出的框架旨在提高非法期刊及出版实践检测的准确性、可扩展性和透明度，同时促进对学术出版格局更全面的理解。
英文摘要：
Predatory journals pose a significant challenge to the integrity of the Open Access (OA) publishing model by exploiting its framework for financial gain while bypassing essential editorial and peer-review standards. This study critically evaluates existing methodologies for identifying such journals, ranging from manual blacklist checks to advanced automated approaches utilizing machine learning. The analysis highlights critical limitations, including the lack of a universally accepted definition of predatory journals, over-reliance on binary classification systems (e.g., blacklists and whitelists), and issues with scalability, reliability and interpretability. To address these shortcomings, this paper introduces a novel methodology based on multivariate graph analysis. By modeling the academic publishing ecosystem as a network of interconnected entities (such as authors, articles, journals, and publishers), this approach provides broader insights into the dynamics of scholarly communication and could help identify illegitimate publishing practices by utilizing graph algorithms like centrality measures, community detection, and anomaly detection. The proposed framework aims to enhance the accuracy, scalability, and transparency of detection of illegitimate journals and publishing practices while fostering a more comprehensive understanding of the academic publishing landscape.
