AI evidence extraction

Policies and Guidelines for the Use of Artificial Intelligence in Latin American Journals Indexed in Scopus and Classified According to the Scimago Journal Rank (SJR)

Cristian Zahn-Muñoz, Patricio Viancos, Nancy Alarcón-Henríquez, Bastián Aravena-Niño, Ezequiel Martínez-Rojas · Publications · 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/10.3390/publications14010017

Methodology & findings

Study design

Predominantly quantitative methodological approach complemented by descriptive documentary analysis.

Sample

N = 1119, 8 groups

Primary method

Descriptive statistics including absolute and relative frequencies; Mann-Whitney U test for inferential analysis to explore statistically significant differences in adoption of AI guidelines according to journal quartile, country of publication, and field of knowledge. Statistical analyses performed using SPSS version 21.

Main result

The study found that "72.8% (815 journals) do not include explicit guidelines regarding the use of AI on their websites, whereas 27.2% (304 journals) explicitly address the use of AI." Among journals with AI guidelines, "97.4% permit the use of artificial intelligence tools, while 2.6% (8 journals) explicitly prohibit their use." Additionally, "54.4% of the 296 journals explicitly specify the permitted uses of AI tools" and "34.1% of journals specify uses that are prohibited for authors," indicating that "a clearer definition of permitted uses compared to prohibited uses" exists.

Reports effect sizes and confidence intervals.

Research paradigm

Positivist/empiricist with descriptive documentary analysis

Author conclusions

The authors conclude that "although the debate surrounding the use of AI in scientific publishing is increasingly recognized, its translation into explicit editorial policies remains incipient and heterogeneous." They state: "A key finding is the gap between the rapid incorporation of AI tools into scientific production—documented in multiple studies cited in this work—and the capacity of journals in the region to regulate their use in a clear, coherent, homogeneous, and transparent manner." Furthermore, they note that "Existing guidelines reflect an emerging consensus on excluding AI systems from authorship and preserving human intellectual responsibility, alongside a regulated openness to the use of these tools as forms of technical support." The authors conclude that "strengthening clear and accessible policies represents a growing challenge and should be considered a priority in the Latin American context."

Risk of bias

Manual coding process with potential subjective bias; Selection bias: Study limited to journals indexed in Scopus/SJR, excluding other databases (SciELO, Latindex); Information bias: Reliance on publicly available website information only; internal policies not captured; Temporal limitation: Single-point-in-time data collection in rapidly evolving regulatory landscape; Missing data handling: Link rot and inaccessible webpages addressed with alternative strategies but some cases excluded; No inter-coder reliability assessment (Cohen's kappa not calculated); Univariate analysis approach limits ability to control for confounders like publisher type and discipline; Manual data collection and coding introduces subjectivity and potential bias without formal inter-coder reliability measures (Cohen's kappa not calculated); Selection bias: Limited to journals in Scopus/SJR database; excludes journals in other databases (SciELO, Latindex, others); Information bias: Relies exclusively on publicly available website information; does not capture internal policies not published online; Temporal limitation: Single point-in-time data collection; cannot assess policy evolution in rapidly changing landscape; Potential role of large publishing houses implementing centralized policies may confound quartile effects; Limited control of confounding variables: Publisher type and disciplinary variation not systematically controlled; Manual data collection and coding may introduce subjectivity; Absence of inter-coder reliability testing (no Cohen's kappa reported); Selection bias: only journals indexed in Scopus/SJR included; Information bias: reliance on publicly available website data only; Potential link rot and inaccessible webpages during data collection; Single point-in-time data collection limiting dynamic assessment

Limitations

The study notes that "data collection and categorization were conducted manually, which may introduce a degree of bias and subjectivity." Furthermore, "the analysis relied exclusively on information publicly available on journals' official websites at the time of data collection
therefore, the absence of explicit AI-related guidelines does not necessarily imply the absence of internal policies." Additionally, "the study was also limited to journals indexed in Scopus and classified according to the Scimago Journal Rank (SJR), which constrains the generalizability of the findings to journals included in other databases, such as SciELO or Latindex." The authors also note that "formal inter-coder reliability measures (e.g., Cohen's kappa) were not calculated, which represents a limitation of the study."

Open questions raised

Limited research on Latin American context; most literature focuses on high-impact publishers in Europe and North America
Need for more detailed analysis of combined effects of variables such as quartile, country, publisher type, and field of knowledge
Lack of longitudinal studies examining evolution of editorial policies over time
Limited understanding of internal policies not publicly disclosed
Need for standardized policy frameworks for AI use in scientific publishing
Requirement for policies specific to reviewers and editors roles

Extracted from: pdfAgreement 65%

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