Stud Hist Philos Sci. 2026 Mar 30;117:102151. doi: 10.1016/j.shpsa.2026.102151. Online ahead of print.

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