AI evidence extraction

Whose English counts? Linguistic justice, gatekeeping, and AI-mediated language policy in academic publishing

Mohamad Almashour, Hesham Aldamen, Marwan Jarrah · Current Issues in Language Planning · 2026

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

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/14664208.2026.2685636

Methodology & findings

Study design

Qualitative document analysis of 10 author-facing policy texts from six major publishing ecosystems, combining structured qualitative content coding with reflexive thematic analysis.

Sample

N = 10, 1 group

Primary method

Structured qualitative content coding with reflexive thematic analysis.

Main result

The study identifies "four recurring policy logics: conditional permission for AI-assisted language improvement, prohibition of AI authorship, individualized author responsibility, and uneven disclosure thresholds." Additionally, the analysis reveals that "AI does not simply lower linguistic barriers to publication. Rather, it reworks standard language ideology, redistributes compliance burdens, and subjects multilingual scholars to new forms of suspicion, disclosure risk, and credibility assessment within the infrastructures of scholarly publishing."

Reports effect sizes.

Research paradigm

Qualitative/Interpretivist

Author conclusions

The authors conclude that "AI does not simply lower linguistic barriers to publication. Rather, it reworks standard language ideology, redistributes compliance burdens, and subjects multilingual scholars to new forms of suspicion, disclosure risk, and credibility assessment within the infrastructures of scholarly publishing."

Risk of bias

Selection bias: Only 10 policy texts from 6 major publishers analyzed; may not represent full diversity of publishing ecosystems or smaller/regional publishers; Document analysis bias: Interpretation of policy texts subject to coder interpretation and reflexivity challenges; Scope limitation: Focus on author-facing policies may not capture editorial or peer-review implementation practices

Data: not_statedCode: not_statedExtracted from: pdfAgreement 84%

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Scholarly Publishing Scholarly Infrastructure Qualitative