Publications

The manuscript cover letter is a consequential but understudied genre: it is the last document an author prepares before submission and often the first an editor reads. This article argues that, as an occluded genre, invisible in the scholarly record, its demands fall unevenly on non-native English speakers (NNES). Synthesizing literature across genre theory, intercultural rhetoric, and AI-assisted writing via a structured narrative review, this article makes three contributions. First, it shows that the cover letter compounds two disadvantages for NNES authors: an occluded genre learned without visible models, and a form of calibrated self-promotion whose register is culturally asymmetric. Second, it advances a dual-beneficiary framing, arguing that assistance that improves cover letter quality benefits authors and editors simultaneously, since better-prepared letters support efficient editorial screening in an era of increased submission volumes. Third, it reviews empirical evidence on large language model performance to propose a four-consideration framework for effective, responsible AI use, offered as a starting point for practice rather than a validated instrument. The article positions academic librarians and research support professionals as the primary audience to translate this framework into publishing consultations and outlines a research agenda to measure the posited editorial efficiency gains and to field-test the framework through ongoing advisory practice.

International indexing remains a significant challenge for university journals, particularly in low- and middle-income countries, where editorial teams often operate with limited institutional support. This study investigated whether meeting international indexing standards depends solely on editorial performance or also on institutional capacity. A multidimensional assessment was conducted on 17 scientific journals published by the National Autonomous University of Honduras (UNAH), using the editorial criteria of Latindex, DOAJ, SciELO, Scopus, and Web of Science. Four dimensions were evaluated: General Characteristics (D1), Editorial Policies (D2), Content Quality (D3), and Digital Visibility (D4). Differences between dimensions were analyzed using the Friedman test with pairwise Wilcoxon signed-rank post hoc comparisons and Bonferroni correction, while hierarchical cluster analysis identified editorial development profiles. Content Quality showed significantly higher compliance than Editorial Policies and Digital Visibility (p < 0.05), with no significant differences between the latter two dimensions. Three editorial profiles (Advanced, Developing, and Critical) revealed marked differences in editorial maturity. Overall, the findings align with the Editorial Autonomy Hypothesis, suggesting that international indexing compliance may be associated not only with editorial competence but also with the institutional capacity required to sustain modern scientific publishing systems, though causal attribution requires future longitudinal and comparative research. This framework offers practical guidelines for strengthening university journals and enhancing their international visibility.

Generative artificial intelligence (GenAI) has gained prominence in scholarly communication, yet its lexical classification, disciplinary diffusion, semantic organization, and association with citation-based recognition remain understudied. This study analyzes 487,753 research and review articles from OpenAlex (August 2019–March 2026), combining rule-based lexical classification with interrupted time-series models, disciplinary mapping, keyword co-occurrence analysis, and citation-count models. The results show a discrete level change in the visibility of GenAI terminology after November 2022. This discontinuity remained robust under classification-error sensitivity analyses, whereas inference regarding the subsequent slope was more sensitive to classification assumptions. The proportion of GenAI-classified publications captured by high-specificity lexical rules increased from 86.65% before December 2022 to 93.64% afterward, consistent with greater lexical concentration. GenAI classification was broadly but unevenly distributed across fields, with the highest prevalence in Computer Science (49.42%) and Decision Sciences (38.57%). A curated keyword network showed overlapping thematic concentrations rather than sharply separated semantic subfields. Month-adjusted citation models showed a positive association between high-specificity classification and citation counts, with a smaller association with GenAI citations after December 2022. Overall, the study characterizes the temporal, lexical, disciplinary, thematic-network, and citation-related dimensions of GenAI’s emergence in scholarly communication.