Data Brief. 2026 Sep 3;69:113224. doi: 10.1016/j.dib.2026.113224. eCollection 2026 Aug.

ABSTRACT

Medieval and Renaissance Latin geographical works constitute a major source for understanding how space, places, and territories were described and conceptualised in pre-modern Europe. However, information about these works, their manuscript transmission, and the places they mention remains dispersed across catalogues, archives, and specialised scholarship. Here we present the IMAGO knowledge graph, a semantically structured dataset representing 343 Latin geographical works written between the 6th and the 15th centuries. The dataset integrates curated information provided by domain experts, including authors, works, manuscripts, printed editions, libraries, literary genres, and mentioned places. Data were initially collected in tabular form and subsequently enriched through semi-automatic reconciliation with external authority sources such as Wikidata and the MIRABILE digital archive. Domain experts further expanded the dataset using a dedicated annotation tool. The curated data were transformed into an OWL 2 DL knowledge graph aligned with the IMAGO ontology and published following FAIR and Linked Open Data principles. The knowledge graph was validated through automated reasoning, expert review, and query-based evaluation. The resulting dataset enables systematic exploration of textual, bibliographic, and spatial relationships within medieval and Renaissance geographical literature and supports reuse in historical, philological, and digital humanities research.

PMID:42757014 | PMC:PMC13583948 | DOI:10.1016/j.dib.2026.113224

Ambix. 2026 May-Aug;73(2-3):205-238. doi: 10.1080/00026980.2026.2692190. Epub 2026 Sep 4.

ABSTRACT

Martin Ruland the Younger's Lexicon Alchemiae (1612) is one of the most influential alchemical dictionaries of the early modern period, yet its sources and compilation methods remain poorly understood. This study applies computational approaches to investigate the vocabulary underlying Ruland's lexicon and to identify potential textual influences. Using a TEI-XML encoded version of the Lexicon Alchemiae, a standard data format for encoding textual data in the digital humanities, we extract its headwords and compare them against large-scale digital corpora of Latin literature, including the Early Modern Latin Alchemical Prints (EMLAP) dataset and the broader GreLa database. By combining frequency-based lexical comparison with contextual word embeddings generated through a Latin BERT (Bidirectional Encoder Representations from Transformers) model, the analysis traces both the distribution and semantic behaviour of terms across earlier alchemical and scientific texts. The article is accompanied by an interactive web application allowing readers to explore additional case studies. Our analysis indicates that Ruland drew not only on earlier Paracelsian word lists, but also on large-scale contemporary compilations such as Andreas Libavius's Alchemia (1597), suggesting that the Lexicon Alchemiae should be understood within a broader movement to systematise and professionalise alchemical knowledge in the late sixteenth and early seventeenth centuries.

PMID:42695817 | DOI:10.1080/00026980.2026.2692190

Stud Hist Philos Sci. 2026 Aug 22;119:102196. doi: 10.1016/j.shpsa.2026.102196. Online ahead of print.

ABSTRACT

This article examines the use of computational tools in the history of science. For this reason, a historical case study is chosen: the interaction between Benjamin Franklin and Jean-Antoine Nollet over their competing theories of electricity in 18th-century France, focusing on the reception of Franklin's one-fluid theory in a Nollet-dominated Parisian Academy of Sciences. We built a database to study the reception and eventual acceptance of Franklin's theory in France. The historian of science John L. Heilbron portrays the Academy as divided and paralyzed, while Roderick W. Home argues that Nollet's dominance led the Academy to conduct the debate in Nollet's shadow. Using a database of publications on electricity related to the Parisian Academy between 1745 and 1785, we examined Franklin's theory in France quantitatively, revealing a pattern of influence and intellectual dominance that changed abruptly after Nollet's death. We also produced data comparing Nollet and another important figure in 18th-century French electricity, Jean-Baptiste Le Roy, and reconstructed this debate using statistics. Our findings demonstrate that databases and scientometric methods can be fruitfully applied in the history and philosophy of science. In particular, we can confirm part of the dynamics of this historical event, especially related to Nollet's influence within the Parisian Academy, an extra-scientific factor. For further exploration of this case study and to test these tools more thoroughly, a larger database of electricity-related publications in France is envisioned as the next step.

PMID:42632168 | DOI:10.1016/j.shpsa.2026.102196

Ambix. 2026 May-Aug;73(2-3):117-144. doi: 10.1080/00026980.2026.2709974. Epub 2026 Aug 12.

ABSTRACT

The promise of computational methods opens new epistemic horizons. Yet, as we discuss in this introduction to the special issue on computational approaches to the histories of alchemy and chemistry, it also brings new epistemic responsibilities. We situate this work within a broader digital and computational history, exploring its relationship with earlier traditions of quantitative history as well as with developments in digital humanities and computational humanities. We distinguish between digital, computational, algorithmic, and AI-driven approaches and we clarify methodological and epistemological concerns that keep the historian firmly in the loop. Claims about computational methods often imply that they enable entirely new modes of inquiry. Yet such sweeping claims can also obscure the limitations and conditions under which these methods operate. This introduction therefore explores both the opportunities and the constraints of computational approaches that more celebratory accounts sometimes overlook. It introduces the individual contributions to the special issue and concludes with a call for responsibility in the adoption of computational methods. We argue for making the histories of alchemy and chemistry more critical, global, and reflexive, while remaining attentive to the assumptions, limitations, and implications of the computational tools we employ.

PMID:42583879 | DOI:10.1080/00026980.2026.2709974

Front Artif Intell. 2026 Jul 28;9:1873672. doi: 10.3389/frai.2026.1873672. eCollection 2026.

ABSTRACT

Sanskrit compound word splitting is challenging because Sandhi-driven phonological transformations obscure word boundaries, making splitting non-deterministic, especially in multi-split cases where multiple hidden boundaries must be identified and constituent segments reconstructed. In this study, a joint end-to-end Transformer-based multi-task architecture is proposed to address this problem by integrating boundary detection and segmented sequence generation within a unified framework. The model employs a shared character-level Transformer encoder that feeds a BiLSTM-CRF boundary prediction branch, which produces sequence-consistent boundary locations and posterior marginals, and a Transformer decoder that generates the segmented output autoregressively. To couple the two tasks, boundary probabilities and entropy-based uncertainty are derived from the CRF marginals. These signals are used to gate encoder representations and to construct a global boundary-aware context that conditions each decoding step, thereby enabling more robust decoding under boundary ambiguity. Experimental results demonstrate consistent improvements over state-of-the-art methods. The model achieves exact-match boundary location accuracy of 84.39%, exact segmentation accuracy of 79.34%, and character-level accuracy of 87.79%. It also achieves boundary-level Precision, Recall, and F1 scores of 92.84%, 91.66%, and 92.25%, respectively. These results indicate that uncertainty-aware coupling and structured BiLSTM-CRF supervision improve segmentation performance while maintaining strong boundary detection, thereby enabling more accurate morphological analysis for NLP and digital humanities applications.

PMID:42582249 | PMC:PMC13457376 | DOI:10.3389/frai.2026.1873672

Data Brief. 2026 Jul 16;68:113086. doi: 10.1016/j.dib.2026.113086. eCollection 2026 Aug.

ABSTRACT

India is one of the countries where many regional languages are spoken in different parts of the country. Each part of the country has its unique cultural traditions; therefore, an abundance of cultural texts is available in Indian Regional languages. The cultural texts are mostly ancient scriptures that represent the community's heritage, values, and ancient knowledge. Braj is one of the Indian regional languages that represents the cultural heritage of the Braj region. The BrajText-Saar dataset contains texts from the Braj language, which features a variety of prehistoric scripts. The data was collected from the Maan Mandir trust's portal, which operates to maintain Braj culture and heritage. The Braj text primarily explains the divine actions and the life of Lord Krishna in the form of poetry and devotional songs. Offline Braj literature from renowned writers is available in manuscript form. This represents an opportunity for cultural text mining in the Braj Language. The developed dataset opens new paths in various research areas, including language studies, sentiment analysis, and emotion analysis, and is also suitable for digital humanities research.

PMID:42564907 | PMC:PMC13446094 | DOI:10.1016/j.dib.2026.113086

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

Ambix. 2026 May-Aug;73(2-3):145-174. doi: 10.1080/00026980.2026.2668735. Epub 2026 Jun 25.

ABSTRACT

This article documents the creation of EMLAP (Early Modern Latin Alchemical Prints), a machine-readable corpus of one hundred Latin alchemical printed works produced within the TOME (The Origins of Modern Encyclopaedism, 2023-2025) project. It first situates the corpus within both the digital humanities landscape and the historiography of alchemy, where the availability of reliable machine-readable texts remains limited. It then addresses the challenges of converting early modern Latin printed text into machine-readable format with a high standard of quality. The article argues that producing a high-quality transcribed corpus at scale still requires human scholarly intervention, and that a transcription project must balance the ideal of digital edition standards against the practical constraints of time and resources. The article describes the practical experience of building EMLAP: the selection of the Transkribus platform for AI-powered automatic text recognition, the choice of transcription models, the development of human quality standards to complement automated metrics, and the construction of a computational pipeline to process and enrich the transcriptions. The EMLAP corpus has been made publicly available in open access (Zenodo repository) as well as in the form of a website that offers different search opportunities for researchers.

PMID:42345208 | DOI:10.1080/00026980.2026.2668735

Sci Data. 2026 May 30;13(1):1237. doi: 10.1038/s41597-026-07527-2.

ABSTRACT

The automatic extraction of geographical entities and spatial relationships from historical texts is a fundamental task for Named Entity Recognition (NER) and relation extraction (RE), with important implications for historical geography and digital humanities. Classical Chinese documents describing ancient cities pose particular challenges due to archaic language, implicit spatial expressions, and complex entity hierarchies. In this study, we present a manually annotated dataset designed for joint geographical entity and spatial relationship extraction from texts related to Lin'an, the capital of the Southern Song Dynasty. The dataset consists of 18 in-domain and 1 out-of-distribution historical documents comprising approximately one million Chinese characters, annotated with 24 categories of geographical entities and 34 types of spatial relationships. This dataset provides a valuable resource for advancing NER and spatial relation extraction in historical texts and supports future research in historical Geographic Information Systems (GIS), cultural geography, and digital heritage reconstruction.

PMID:42225712 | PMC:PMC13522433 | DOI:10.1038/s41597-026-07527-2

Data Brief. 2026 May 10;66:112835. doi: 10.1016/j.dib.2026.112835. eCollection 2026 Jun.

ABSTRACT

This dataset was collected during on-site fieldwork conducted in the district of Chinchero, located in the province of Urubamba, Cusco, Peru, a region internationally recognized for its rich Andean textile tradition rooted in Inca Culture heritage. The dataset comprises high-quality Photographic images of traditional handwoven Andean textile iconographies produced by local artisan communities. These images were captured directly at textile centers where the fabrics are woven, dyed and finished using ancestral techniques measuring authentic representation of colors, textures, and symbolic patterns under natural and controlled conditions. The dataset consists of 1358 images organized into 28 distinct classes, each corresponding to a specific textile iconography characteristic of the Chinchero tradition. The images are provided in a processed and curated format, facilitating organization enables systematic analysis of visual motifs that are often challenging to distinguish due to their intricate geometric patterns and cultural symbolism. The primary reuse potential of this dataset lies in its application to Artificial Intelligence (AI) and Machine Learning (ML) research focused on image classification, pattern recognition, and cultural heritage preservation. Researchers can leverage the dataset to develop and evaluate models capable of identifying and differentiating traditional Andean textile iconographies, addressing the growing difficulty faced by younger generations, local communities, and visitors in recognizing the cultural expressions. Additionally, the dataset supports interdisciplinary research in digital humanities, ethnography, textile studies, and cultural informatics contributing to the documentation and preservation of intangible cultural heritage. By making this dataset publicly available, this work aims to support the development of AI-driven tools for cultural preservation, educational applications, and heritage awareness, while fostering collaboration between researchers, technologists, and local artisan communities to safeguard ancestral knowledge for future generations.

PMID:42220648 | PMC:PMC13217882 | DOI:10.1016/j.dib.2026.112835

Sci Rep. 2026 May 21;16(1):23240. doi: 10.1038/s41598-026-52522-7.

ABSTRACT

Traditional Chinese paintings pose unique challenges for computational emotion analysis due to culturally-specific aesthetic principles that differ fundamentally from Western art paradigms. This study proposes an attention-guided CNN-Transformer hybrid model that integrates local feature extraction with global contextual modeling. The architecture employs spatial, channel, and cross-attention modules to fuse CNN-extracted brushwork details with Transformer-captured compositional relationships. Evaluated on a dataset of 7842 traditional Chinese paintings across seven emotion categories-tranquility, melancholy, vigor, elegance, desolation, joy, and solemnity-the model achieves 91.4% classification accuracy. Comparative experiments demonstrate superior performance over ResNet-101, DeiT-B, and ConvNeXt-T baselines. Ablation studies confirm the critical role of the attention-guidance module, while visualization analysis reveals alignment with traditional art theory principles. These results provide empirical support for domain-specific architectural designs in culturally-sensitive visual analysis within Han Chinese literati painting traditions, with generalizability to broader artistic domains constituting an important direction for subsequent investigation.

PMID:42168395 | PMC:PMC13402820 | DOI:10.1038/s41598-026-52522-7

Ann Sci. 2026 May 6:1-27. doi: 10.1080/00033790.2026.2638942. Online ahead of print.

ABSTRACT

This essay investigates the reception of William Gilbert's foundational work on magnetism, De Magnete, through a comprehensive analysis of extant copies of its early modern printed editions (1600, 1628, 1629, 1633). By employing a hybrid methodology combining quantitative and qualitative approaches to readers' annotations, this study charts patterns of engagement with Gilbert's text across diverse contexts and intellectual traditions. While celebrated for its experimental innovations and practical applications in navigation, it also elicited cosmological and humanist interests. Statistical analyses of readers' marks demonstrate a skewed distribution of engagement, with the majority of annotations concentrated in a small fraction of extant copies. This study moreover contributes to the historiography of early modern science by illustrating the methodological potential of combining large-scale digital datasets with close textual analysis, advocating for more systematic, collaborative approaches to the history of reading and book culture. In addition, a near-complete census of copies of De magnete is provided.

PMID:42091227 | DOI:10.1080/00033790.2026.2638942

Int J Archit Comput. 2024 Jan 13;22(3):277-294. doi: 10.1177/14780771231220903. eCollection 2024 Sep.

ABSTRACT

This article elaborates a computationally enabled approach to the study of design methods in 1960s North America. This entails the construction, visualization, and analysis of a digital database built from entries of the Design Methods Group Newsletter, a periodical published monthly between 1966-71. The article proposes a workflow that combines methods such as topic modeling and network visualization to activate the Newsletter as a source of anecdotal and informal knowledge, and to enable histories of connectivity and transaction that may elude archival investigations on singular actors or institutions. In doing so, the article contributes arguments and techniques for the study of design methods as a complex social, technical, and intellectual meshwork. The meshwork brings discursive themes, techniques, actors, and institutions at the same level of investigation and allows for layered cartographies of the field that advanced the systematic study of design and ushered in the development of early computer applications.

PMID:42038932 | PMC:PMC13104777 | DOI:10.1177/14780771231220903

PLoS One. 2026 Apr 21;21(4):e0347253. doi: 10.1371/journal.pone.0347253. eCollection 2026.

ABSTRACT

The overseas reception of classical literature through online platforms presents a critical lens for understanding cross-cultural dynamics in the digital age. This study investigates the overseas reception of English translations of Journey to the West by analyzing a corpus of 1,795 reviews from Amazon and Goodreads to examine temporal dynamics, cross-platform sentiment patterns, and topic modeling. The analysis covers four celebrated translators: Arthur Waley, Anthony C. Yu, Julia Lovell, and W.J.F. Jenner. Methodologically, we developed a hybrid sentiment lexicon by integrating a domain sentiment lexicon with AFINN, NRC, and VADER through weighted fusion, addressing the limited adaptability of general sentiment lexicons in translated literature analysis. LDA modeling was further applied to enable data-driven theme extraction. Key findings reveal a consistent year-on-year increase in review counts across all translations. Notably, despite an overall positive sentiment, significant cross-platform divergences emerge, reflecting the distinct evaluative mechanisms of digital platforms. Thematic analysis identifies three central reader concerns: translation quality, plot acceptance, and character portrayal, with plot acceptance exhibiting markedly higher negativity. Furthermore, translator-level analysis reveals performance variations across these themes. This study demonstrates how digital platforms reconfigure the valuation of literary translation, and pioneers a methodological framework for capturing the dynamic interplay between reader perception, media infrastructure, and textual mobility, offering new pathways for digital humanities research in translation studies.

PMID:42013105 | PMC:PMC13098970 | DOI:10.1371/journal.pone.0347253

Med Health Care Philos. 2026 Apr 16. doi: 10.1007/s11019-026-10347-1. Online ahead of print.

ABSTRACT

The uptake of social science methods by bioethics significantly expanded its methodological spectrum, raising new theoretical, methodological, and practical questions. Recently, we are witnessing another trend, adding advanced data science methods to bioethics' toolkit to aid, for example, in online data analysis, support scholarly writing, and inform clinical ethics. This article explores the emerging field of Digital Bioethics across its dimensions by analysing the tangled relationship between topics and methods, highlighting intersections between Digital Bioethics and Bioethics of the Digital, and advocating for a methods-based definition of the field. The use of advanced data science methods within bioethics must be interpreted in the context of the use of Artificial Intelligence (AI) in health care. At the same time, it presents unique opportunities and challenges. Defining, and thus demarcating, Digital Bioethics can create support for the new field but also requires navigating trade-offs. To do so, we take four kindred academic fields as points of comparison (Digital Humanities, Experimental Philosophical Bioethics, computational medicine and digitised biology) to analyse what each of them teaches for critically assessing and further developing Digital Bioethics. The article discusses potential pitfalls and concludes with recommendations on how the field can fully develop its potential to promote bioethical research and argument. Furthermore, the article discusses how a critical reflection of the use of AI methods within bioethics itself will also contribute to the ethical oversight of increasingly AI-driven branches of healthcare.

PMID:41989660 | DOI:10.1007/s11019-026-10347-1

Stud Hist Philos Sci. 2026 Jun;117:102151. doi: 10.1016/j.shpsa.2026.102151. Epub 2026 Mar 30.

ABSTRACT

This paper examines large language models (LLMs) as research tools in the history, philosophy, and sociology of science (HPSS). Because LLMs can work directly with heterogeneous, unstructured texts and capture meaning-relevant associations from usage patterns, they offer new ways to bridge close reading and corpus-scale analysis, challenging the idea that computational scale and interpretive nuance must trade off. We provide a compact primer on LLMs, covering the main components of their neural network architecture, the differences between generative and full-context models, and adaptation strategies such as fine-tuning, prompt-based learning, and retrieval-augmented generation (RAG). Building on this foundation, we analyze how LLMs recast three classic methodological problems in HPSS: working with historically messy data, detecting and interpreting large-scale patterns, and modeling scientific change over time. Across these areas we synthesize recent work in HPSS and adjacent fields, and we clarify how LLM outputs can function as exploratory prompts, as inputs to more structured pipelines, or as evidence under stricter validation and documentation. We conclude with four lessons: 1) model choice embeds interpretive trade-offs, 2) responsible use requires LLM literacy, 3) HPSS should develop its own tasks and evaluation practices, and 4) LLMs should extend rather than replace established interpretive methods. We also situate these methodological questions within broader concerns about platform dependence, accountability, and the responsibilities attached to research infrastructures. Finally, we argue that HPSS is well positioned to both use LLMs and to interrogate what counts as explanation, evidence, and responsible use in interpretive research.

PMID:41916166 | DOI:10.1016/j.shpsa.2026.102151

Int J Transgend Health. 2025 Mar 15;27(2):902-916. doi: 10.1080/26895269.2025.2478092. eCollection 2026.

ABSTRACT

BACKGROUND: Research on the challenges, marginalization, and identity of trans women in India has sparked important discussions, contributing to progressive changes in society. While the visibility and recognition of trans women is steadily growing, beneficial schemes tailored to their unique challenges are often overlooked, underscoring the need for greater attention and action.

AIM: The article aims to identify the unique healthcare and migration challenges faced by South Indian trans women and the reach and utilization of government-provided facilities by their community.

METHOD: A survey of 53 and interviews with 4 South Indian trans women focused on the utilization of state and central government schemes. Data from public and private healthcare facilities in Madurai were collected and visualized using Airtable, with results disseminated in Tamil and English to ensure accessibility for the trans community.

RESULTS: A relationship was identified between the effectiveness of state government welfare schemes and the well-being of the trans women's community. However, central government schemes often fail to reach their entire population. Furthermore, state government transportation schemes do not sufficiently support their healthcare access and economic development.

DISCUSSION: To enhance the socio-economic development of trans women, policymakers can ensure that beneficial schemes comprehensively reach all segments of society. Increased promotion, awareness, and advancements in these schemes are necessary to meet the needs of the trans women community. Additionally, extending free bus fare facilities specifically to trans women is recommended to improve their healthcare access, mobility, economic opportunities, and integration into mainstream society.

PMID:41891076 | PMC:PMC13015021 | DOI:10.1080/26895269.2025.2478092