Many digital humanities projects involve tedious and repetitive tasks that take time away from higher-level tasks further down the pipeline that require intelligent decision making. When funding is available, tedious and repetitive tasks are often assigned to research assistants, but when funding is scarce, those tasks tend to create bottlenecks that either impede progress or halt it altogether. This article argues that artificial intelligence and machine learning tools and techniques are worth exploring as cost-effective, accessible solutions to these problems. The Digital Latin Library project provides a case study through its experiments with fine-tuning pretrained transformer language models to process noisy bibliographic metadata. The results show that the models have potential for accelerating this tedious task, but that the experiments also had an unexpected, if positive, outcome: the models revealed gaps in the catalogue’s coverage, helping to focus the efforts of the human experts working on the project.

This comparative study locates Frederick Douglass’s (1818–95) religiosity within his published autobiographies as well as his 1886–7 unpublished travel diary – taken as part of the Library of Congress’s Frederick Douglass Papers (1841–64). R-scripted text analysis of Douglass’s expressed faith is foregrounded through the generation of clean digital transcriptions using handwritten text recognition (HTR), the process of converting non-consistent images into computer-readable text. In training a HTR model to 90.60 per cent character accuracy and manually correcting its outputs, this article forms a practical study in foregrounding automated transcription to benefit archival research and leverages multiple technologies to uncover lexical patterns across disparate autobiographical works. In showing that the abolitionist’s introduction of humanistic themes holds little dependence to instances of religious expression, we challenge scholarly interpretations that Douglass committed to a hard pivot away from religious language and placed God ‘off-the-stage’ in his later works. In doing so, our approach demonstrates the range of techniques available to researchers auditing differences between private handwritten archives and public published accounts. This article therefore holds significance in documenting a scalable text extraction method on handwritten materials, digitized from smaller analogue library collections, while also advancing historical knowledge of Douglass’s use of religious language.

This article draws upon recent developments in cognitive neuroscience and natural language processing to contribute a techno-cognitive perspective into the ‘deep reading’ versus ‘surface reading’ debate in literary studies. Research at the intersection of humanities and sciences suggests that narrative experience, including both production (decoding) and reception (encoding) of stories, constitutes a sequentially and hierarchically complex process shaped simultaneously by socio-cultural contexts, sensory-emotional dynamics, and cognitive integration across multiple levels of complexity. This interdisciplinary view contrasts with traditional humanities methodologies such as area studies, which privileges identity-based accounts of literary phenomena, or Marxist genealogy, which neglects extra-political sources of meaning. The article surveys relevant research findings across multiple domains and discusses the hermeneutic implications of the techno-cognitive approach for literary studies, exemplified in a reading of Zhang Xianliang's 1985 novel Half of Man is Woman.

This article examines whether authors change their styles when writing in different genres, like poetry and prose, particularly under the metrical constraints of the former. Using a corpus of works by five modern Egyptian authors, we analyse the effectiveness of N most frequent words and N most frequent morphological segments in distinguishing between poetry and prose written by the same author. Through supervised and unsupervised machine learning techniques, we demonstrate that authors exhibit distinct stylistic fingerprints in each genre, influenced by the unique conventions and constraints of each form. Results show that each author uses two different stylistic prints when writing prose and poetry. However, when mixing poetry and prose together, all genre-related texts cluster separately, potentially causing author obfuscation. Findings also show that the frequent word method is sufficient for accurately attributing authorship when it comes to mixed-genre texts. In short, the tested linguistic standard features prove resilient across different genres and even survive the constraints of formal poetic meter.

This article explores how geocomputational processes can be utilised to re-create historical spatial boundaries using Irish historical data. For this study, a series of spatial objects representing Irish Republican Army (IRA) brigades in County Limerick during 1921 were re-created in R using the Military Service Pension Collection (MSPC), one of the largest archival projects released by the Military Archives of Ireland. Using density equalising projections via cartograms, a series of analyses of the demography of the IRA was undertaken using these internal boundaries. County Limerick along the western seaboard of Ireland was chosen as a case study, as it represents a mixed urban/rural environment outside of the three major population centres on the island of Ireland: Cork, Dublin and Belfast. By deploying a geocomputational approach via the programming language R, this article provides a roadmap for historians and humanities scholars in general to reproduce these findings and spatial boundaries for future research.