AI evidence extraction

Integrity under pressure: on generative AI, fabricated references and ethical publishing

Marina Joubert, Michelle Riedlinger · Journal of Science Communication · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.22323/388620260304154318

Methodology & findings

Study design

Editorial commentary and literature review of existing scholarship on fabricated references and AI use in academic publishing.

Main result

The paper identifies a "growing problem with fabricated references" stating that "a rise in so-called 'ghost references', fabricated by AI tools" has been observed. Xu et al. [2026] found that "between 14% and 92% of 375,440 AI-generated citations were fabricated to some degree." Case studies demonstrate how "fabricated references can pass undetected through existing publication systems," with examples including Moore [2025] who "analysed a paper published in a reputable journal and found that the majority of its references were fabricated, ultimately leading to the article's retraction."

Research paradigm

Critical/Interpretive

Author conclusions

The authors conclude that "authorship entails ownership, and ownership entails responsibility," emphasizing that "quality control mechanisms cannot replace authors' ethical obligations." They state their commitment: "We will reject, withdraw, or retract manuscripts that contain fabricated" content, grounding this "in safeguarding JCOM's standards and recognising that science communication, as a field of research and practice, has a particular stake in the integrity of knowledge production and sharing."

Risk of bias

Selection bias: The editorial perspective is based on JCOM submissions which may not represent all academic fields or publishing venues; Observation bias: Fabricated references may go undetected, leading to underestimation of the problem; The retrospective nature of case studies limits generalizability

Open questions raised

The authors identify that "because this is a rapidly emerging phenomenon, much of the empirical research on fabricated citations currently appears as preprints," suggesting a need for more rigorous, peer-reviewed studies on AI-generated fabricated references. They also note the ongoing development of automated validation systems and the need for better detection mechanisms.
The paper notes that empirical research on fabricated citations is still emerging, with much appearing as preprints. It identifies the need for better detection systems and stronger editorial safeguards against AI-generated fabricated content.
The paper notes that "because this is a rapidly emerging phenomenon, much of the empirical research on fabricated citations currently appears as preprints," suggesting gaps in peer-reviewed literature on this topic.

Extracted from: pdfAgreement 82%

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