Technol Cult. 2026;67(3):973-7. doi: 10.1353/tech.2026.a996506.

ABSTRACT

This article offers the first comprehensive review of women's contributions and gender-focused research in Technology and Culture (T&C) from its early years through 2025. Using a mixed-methods approach that combines distant reading of digital publication datasets with qualitative mapping of milestones and turning points in the history of T&C, the article examines how the gender composition of authors evolved over time, how this has influenced the journal's content, and what broader role feminist theory has played in shaping new scholarly directions in the history of technology. Findings reveal that while women authors and gender-focused studies have steadily increased in number, engagement with feminist theory followed a less linear trajectory. Early historians advanced feminist approaches that influenced fields such as feminist science and technology studies and feminist digital humanities. Yet the normalization of gender analysis within the history of technology did not produce a similarly consolidated feminist historiographical tradition. The article concludes by outlining possible directions for recovering a more explicit feminist historiographical and methodological conversation within the field and across adjacent interdisciplinary domains.

PMID:42544481 | DOI:10.1353/tech.2026.a996506

Children (Basel). 2026 Jul 22;13(7):967. doi: 10.3390/children13070967.

ABSTRACT

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) is increasingly used in youth mental health services, including clinical decision support, risk prediction, and digital therapeutics. However, existing frameworks provide limited guidance for integrating ethical, cultural, and relational considerations into the design, governance, and implementation of AI-enabled mental health technologies. This scoping review examined how digital humanities-informed approaches have been incorporated into AI-supported mental health interventions for children and adolescents.

METHODS: A scoping review was conducted following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. Peer-reviewed literature published between 2015 and 2025 was searched using PubMed and supplemented by semantic searches through Elicit. Systematic reviews, scoping reviews, and meta-analyses examining AI-enabled digital mental health interventions and digital humanities perspectives were included. Data were synthesized using inductive thematic analysis.

RESULTS: Seventeen review-level studies met the inclusion criteria. Six recurring themes were identified: engagement, participatory co-design, human oversight, equity, ethical governance, and implementation. Across the included reviews, humanities-informed approaches were associated with greater attention to relational engagement, stakeholder participation, transparency, contextual adaptation, and culturally responsive implementation. Evidence supporting intervention effectiveness was strongest in systematic reviews and meta-analyses, whereas findings related to ethics, governance, equity, and implementation were derived primarily from scoping reviews and conceptual syntheses.

CONCLUSIONS: This review suggests that digital humanities provides a valuable interdisciplinary perspective for informing the design, governance, and implementation of AI-enabled youth mental health interventions. Although the current evidence base remains heterogeneous, integrating humanities-informed approaches may support the development of AI systems that are more ethical, equitable, and developmentally responsive. Future research should evaluate these approaches through empirical implementation studies and emerging generative AI applications.

PMID:42509992 | PMC:PMC13406150 | DOI:10.3390/children13070967

Sci Rep. 2026 Jul 8;16(1):21193. doi: 10.1038/s41598-026-54438-8.

ABSTRACT

Arabic poet classification presents distinctive challenges stemming from the morphological richness and stylistic diversity inherent in both classical and modern Arabic verse. This study conducts an extensive comparative evaluation of several neural language models to assess their ability to represent poetic expression and capture authorial characteristics. Two carefully curated datasets are utilised: FrequentPoets, representing prolific authors with extensive verse collections, and CrossEraPoets, encompassing poets from distinct historical periods to examine temporal stylistic variation. A comparative evaluation framework is introduced to contrast domain-specific and general-purpose language models across prolific authorship and cross-era stylistic variation. The domain-adapted AraPoemBERT consistently achieves superior performance, attaining 73.11% accuracy (73.00% F1) on FrequentPoets and 77.06% accuracy (77.04% F1) on CrossEraPoets, whereas the general-purpose GPT-4o demonstrates considerably lower performance under zero-shot and few-shot evaluation settings. The results highlight the significance of domain-adapted pretraining for morphologically complex languages like Arabic and suggest the potential advantage of transformer-based architectures in modelling stylistic and linguistic nuances unique to Arabic verse. These findings also suggest that temporal diversity may play an important role in model generalisation across different poetic styles. The study contributes to Arabic Natural Language Processing (NLP) and digital humanities by enabling computational authorship attribution, stylistic analysis, and cross-historical exploration of Arabic literary heritage. Overall, the proposed framework provides a robust foundation for future research in Arabic poetry analytics and domain-specific language modelling.

PMID:42420346 | PMC:PMC13346815 | DOI:10.1038/s41598-026-54438-8

Data Brief. 2026 Jun 19;67:112983. doi: 10.1016/j.dib.2026.112983. eCollection 2026 Aug.

ABSTRACT

This article presents KannadaLit4NLP, a large-scale, machine-readable corpus of Kannada literary texts designed to support natural language processing (NLP) research for a low-resource language. The dataset comprises 24,746 literary verses from three major Kannada literary traditions-Vachanas (11th-19th century), Tripadis (16th century), and Kagga (20th century)-along with 22,369 corresponding interpretations curated from scholarly sources. The corpus captures linguistic, stylistic, and semantic variations across historical periods and literary forms. The dataset was developed through a systematic pipeline that included source identification, digitisation via optical character recognition (OCR), manual verification, and structured annotation. Each entry is organised in a structured format that includes the original verse, metadata (literary form, author, and source), and associated interpretation(s), enabling its use in tasks such as semantic textual similarity, textual entailment, information retrieval, and generative modelling. KannadaLit4NLP addresses the limited availability of culturally grounded Kannada datasets by providing a resource that integrates classical and modern literary content with interpretative annotations. The dataset can facilitate the development and evaluation of NLP models in areas such as semantic understanding, translation, and knowledge representation, while also supporting computational studies of literary and cultural texts. The dataset is made publicly available to encourage further research and reproducibility in Kannada NLP.

PMID:42389175 | PMC:PMC13320459 | DOI:10.1016/j.dib.2026.112983