12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

Citation Integrity

The Citation Integrity theme comprises 248 papers in this corpus published between 1963 and 2026. Work here is dominated by Empirical Study, Benchmarking, Content Analysis. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Empirical Study65 (26%)
  • Benchmarking27 (11%)
  • Content Analysis22 (9%)
  • Literature Review22 (9%)
  • Conceptual18 (7%)
  • Position Paper18 (7%)

Research domains

  • Research Integrity185 (75%)
  • Scholarly Infrastructure30 (12%)
  • Knowledge Synthesis10 (4%)
  • Peer Review7 (3%)
  • Academic Writing6 (2%)
  • Literature Discovery3 (1%)

Frequent sub-topics

technical barriers to copyright assessment in generative AI · 1Data rights and AI training data acquisition legality · 1contract law and copyright implications for AI training data · 1data scraping for AI training and copyright ethics · 1LLM-based plagiarism detection · 1citation formats in generative AI output · 1AI-generated academic citations accuracy · 1AI disclosure trends in corporate filings · 1

Open research gaps

  • The paper identifies the need for: (1) development of targeted training programs for students and educators on effective ChatGPT utilization; (2) research on context-specific applications of ChatGPT f
  • The authors identify the need for improved handling of citations from fields beyond Computer Science with non-standard formats. They also mention the need for a future Scholar Connector router that ro
  • Expanding the retrieval cascade to field-specific repositories for improved coverage in domains with specialized publication infrastructure. Extending the benchmark to naturally occurring LLM-generate
  • Limited understanding of the boundaries between fraud and technological innovation in AI-generated content
  • Lack of comprehensive frameworks for responsible AI adoption in academic settings

Representative papers

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