12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
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Research theme

Authorship & Attribution

The Authorship & Attribution theme comprises 107 papers in this corpus published between 1997 and 2026. Work here is dominated by Empirical Study, Experimental, Position Paper. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Empirical Study28 (26%)
  • Experimental16 (15%)
  • Position Paper14 (13%)
  • Conceptual13 (12%)
  • Benchmarking11 (10%)
  • Literature Review9 (8%)

Research domains

  • Research Integrity81 (76%)
  • Scholarly Infrastructure7 (7%)
  • Academic Writing6 (6%)
  • Peer Review3 (3%)
  • Human-AI Collaboration2 (2%)
  • Bias & Epistemic Risk1 (1%)

Frequent sub-topics

AI and non-human co-authorship in literary prizes, authorial power dynamics, AI-assisted creative writing · 1stylometric feature engineering for classical Arabic texts · 1AI detection tools and authorship attribution reliability · 1Distinguishing human-authored from AI-generated texts; emotion and personality in LLM outputs · 1LLM fingerprinting detection in non-English text · 1historiographical error in authorship of UNESCO Library Manifesto; use of generative AI to understand error perpetuation · 1fine-tuned RoBERTa classifier for AI-generated text detection with interpretability analysis · 1philosophical perspectives on AI-assisted journalism authorship · 1

Open research gaps

  • The authors identify gaps in understanding the robustness and potential biases of LLMs in automatic text scoring tasks. They note that while prior research focused on accuracy, "their robustness and p
  • The paper identifies the need to test robustness of classifiers against "new models or unknown obfuscations" and to evaluate "out-of-domain classification effectiveness of the detectors".
  • The study implies a need for stronger focus on authorship ethics training and institutional mechanisms to address unethical co-authorship practices in academic institutions.
  • The authors identify the lack of standardization in AI disclosure as a critical gap. They note that "the lack of standardization is one of the crucial issues in the field of generative AI" and that "t
  • The authors identify that "Future work employing bibliometric and scientometric methodologies could help determine whether these patterns represent isolated anomalies or emerging systemic trends." The

Representative papers

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