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Michael Mncedisi Willie
Council for Medical Schemes
Economics, Econometrics & Finance Social Sciences

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Identifying AI-Generated Research Papers: Methods and Considerations: Methods and Considerations Michael Mncedisi Willie
Golden Ratio of Data in Summary Vol. 4 No. 2 (2024): May - October
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v4i2.489

Abstract

Recent advancements in natural language generation (NLG) have revolutionized content creation, enabling artificial intelligence (AI) tools to produce coherent and seemingly authentic texts, including scholarly papers. While AI-generated content offers efficiencies in speed and volume, concerns over authenticity, ethical implications, and academic integrity persist. This review explores methods and considerations for identifying AI-generated research papers, emphasizing the need to distinguish between human-authored and AI-generated content to uphold scholarly standards and ensure transparency in research. Key detection techniques include textual analysis, metadata examination, and content evaluation. Ethical concerns regarding AI's role in research are also discussed, underscoring the importance of ongoing research to refine identification methodologies and maintain research integrity.
Citation Cartels: Understanding Their Emergence and Impact on the Academic World Michael Mncedisi Willie
Golden Ratio of Data in Summary Vol. 4 No. 2 (2024): May - October
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v4i2.581

Abstract

Citation metrics have become pivotal in evaluating academic research and influencing funding, promotions, and institutional prestige. However, the increasing emphasis on these metrics has given rise to unethical practices, notably citation cartels, which artificially inflate citation counts through collusive agreements among researchers or journals. This study investigates the prevalence and impact of citation cartels by analysing citation patterns in recent papers. Using a systematic approach, we examined citation data across five papers to identify patterns of collusion and the extent of citation inflation. The results reveal that Author 1 was cited in every reference across four out of five papers, either as a sole author or co-author, with a direct or indirect responsibility for 100% of the citations. Similarly, Authors 2 and 3 demonstrated substantial influence, with median citation shares of 36% and 35%, respectively. These findings highlight the significant role of key authors in shaping citation distributions and raise concerns about the integrity of citation metrics. The study concludes with recommendations for addressing citation cartels, including implementing detection systems, enhanced peer review processes, and a more balanced approach to evaluating research impact.
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