CASRAI Dictionary

Tag: Scholarly Publishing Ethics

  • Generative AI Authorship: Integrity and Disclosure Standards for Academic Journals

    Introduction to AI Authorship in Scholarly Spaces

    The rapid emergence of Large Language Models (LLMs) and Generative AI tools (such as ChatGPT, Claude, and Gemini) has disrupted academic writing and scientific publishing, raising complex ethical questions regarding authorship, copyright, and research integrity.

    Why Generative AI Cannot Be Listed as a Co-Author

    Major global editorial associations, including COPE, ICMJE, and WAME, have established a strict rule: Generative AI tools cannot be listed as co-authors. Authorship carries legal and ethical responsibilities, including accountability for the accuracy, validity, and integrity of the entire study—responsibilities that a software tool cannot assume.

    Drafting Clear Disclosure Guidelines for Researchers

    While AI cannot be an author, researchers may use AI tools to assist with grammar editing, translation, or literature mapping. Journals must mandate complete disclosure. Authors should state: 1. Which AI tool was used. 2. The specific version and developer. 3. The precise prompt parameters and sections of text or code generated.

    Detecting Unethical AI Writing and Protecting peer review

    Editorial teams are deploying automated detectors to identify undisclosed AI generation. Peer reviewers must also play a role, ensuring that AI-written text does not introduce hallucinations, fabricated references, or biased analysis, safeguarding scientific credibility.

    Key Data and Comparative Metrics

    AI Tool Application Authorship/Disclosure Requirement Standard Manuscript Citation Standard
    Drafting Text / Coding Strictly prohibited as author; must disclose in methodology or acknowledgements. Describe tool use in detail: ‘ChatGPT (v4.o) was used to assist with coding…`
    Grammar / Proofreading Generally allowed without disclosure, as long as original meaning is unchanged. Standard acknowledgement of digital editing tools (optional).
    Image Generation Prohibited in clinical/scientific figures; allowed for conceptual art with clear labels. Cite source tool and license parameters in figure caption.

    Actionable Checklist for AI Authorship

    • Incorporate a strict Generative AI Authorship policy into journal instructions.: Incorporate a strict Generative AI Authorship policy into journal instructions.
    • Mandate a dedicated ‘AI Use Disclosure’ statement in all submitted manuscripts.: Mandate a dedicated ‘AI Use Disclosure’ statement in all submitted manuscripts.
    • Explicitly prohibit listing AI tools as co-authors on submission forms.: Explicitly prohibit listing AI tools as co-authors on submission forms.
    • Train peer reviewers to identify AI-hallucinated citations and arguments.: Train peer reviewers to identify AI-hallucinated citations and arguments.
    • Integrate automated AI writing and translation detection tools into workflows.: Integrate automated AI writing and translation detection tools into workflows.

  • Addressing Citation Manipulation and Coercive Citation in Academic Publishing

    Introduction to Citation Manipulation in Scholarly Spaces

    Citations are the currency of academic impact. However, the high-stakes nature of academic metrics has led to unethical practices, including citation manipulation and coercive citation, which distort scholarly evaluation and compromise publishing integrity.

    Defining Forms of Citation Manipulation

    Citation manipulation occurs when authors, editors, or reviewers inflate citation counts unethically. This includes: 1. Citation Rings: Groups of researchers agreeing to cite each other’s papers excessively. 2. Self-Citation Abuse: Authors citing their own unrelated work to boost metrics. 3. Journal Self-Citation Spam: Journals demanding authors cite past papers in their journal to inflate their Impact Factor.

    Coercive Citation by Reviewers and Editors

    Coercive citation is a particularly egregious form of manipulation. It occurs when a peer reviewer or journal editor pressures authors to add citations to their own papers (or to the journal’s papers) as a condition of manuscript acceptance, exploiting their power dynamic over authors.

    Systemic Reforms and Algorithmic Audits

    Combating citation manipulation requires technical and structural reforms. Databases like Scopus and Web of Science monitor citation networks for anomalies, suspending journals that engage in self-citation abuse. Editorial offices should implement blind review audits and train editors to flag reviewer-suggested citations that are irrelevant to the manuscript.

    Key Data and Comparative Metrics

    Manipulation Type Typical Scenario Recommended Ethical Control
    Coercive Citation Reviewer demands 5 citations of their own papers in the review report. Editor reviews report, removes coercive demands before forwarding to author.
    Citation Rings A network of 5 authors cite each other uncharacteristically across journals. Algorithmic network analysis by bibliometric indexers (e.g., Clarivate).
    Self-Citation Abuse Author cites 20 of their own past papers in a 25-citation bibliography. Reviewers audit bibliography relevance, flag unnecessary self-citations.

    Actionable Checklist for Citation Manipulation

    • Adopt the COPE guidelines on citation manipulation within editorial policies.: Adopt the COPE guidelines on citation manipulation within editorial policies.
    • Establish automated checks to monitor journal-level self-citation rates.: Establish automated checks to monitor journal-level self-citation rates.
    • Train editors to scrub reviewer reports of coercive citation demands.: Train editors to scrub reviewer reports of coercive citation demands.
    • Educate authors on their right to refuse irrelevant reviewer citation suggestions.: Educate authors on their right to refuse irrelevant reviewer citation suggestions.
    • De-emphasize raw citation counts in departmental performance reviews.: De-emphasize raw citation counts in departmental performance reviews.