12,637 papers · updated 10 Oct 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 author identifies that "there is almost no ethics literature yet on the question of whether and when the researcher has actually committed any serious research misconduct" regarding AI-produced ma
  • Not explicitly identified in the abstract
  • The authors identify that "To date, however, its [likelihood ratio framework] application has been confined to English-language texts" and that "Combining these diverse approaches through fusion promi
  • The authors identify that existing "zero-shot methods that attempt to operationalize these outputs have met with limited success" and propose that the problem lies in using mean aggregation across tok
  • The paper identifies a gap in longitudinal empirical research on AI authorship perceptions pre- and post-LLM boom, and questions the viability of 'authorship' as a conceptual framework for computer-ge

Representative papers

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