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AI evidence extraction

Letter to editor: Academic journals should clarify the proportion of NLP-generated content in papers

Gengyan Tang · Accountability in Research · 2023

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

9/10
Relevance
0/4
Quality (LMQS)
I
Evidence
29
Citations
6.85
FWCI
Top 10%
Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/08989621.2023.2180359

Methodology & findings

Study design

Opinion-based letter to the editor presenting arguments for editorial policy clarification regarding NLP-generated content in academic papers.

Main result

The letter argues that "if academic journals are willing to accept papers that include NLP-generated content under certain conditions, editorial policies should clarify the proportion of NLP-generated content in the paper." The authors assert that "Excessive use of NLP-generated content should be considered as academic misconduct."

Reports effect sizes.

Research paradigm

Critical/normative (policy advocacy)

Author conclusions

The authors conclude that "if academic journals are willing to accept papers that include NLP-generated content under certain conditions, editorial policies should clarify the proportion of NLP-generated content in the paper" and that "Excessive use of NLP-generated content should be considered as academic misconduct."

Open questions raised

  • The letter identifies the need for academic journals to establish clear editorial policies and guidelines regarding the acceptable proportion of NLP-generated content in papers, and the need to define thresholds for when such content constitutes academic misconduct.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 90%

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