If the same in-game events are arrayed in different sequences, players may find some sequences to be more attractive, exciting, engaging, or intriguing than others. But how can such sequences be explained, understood, planned, compared, or rated? Do models exist that analytically describe various sequences? To address this question, this study seeks to identify conceptual models for the strategic ordering of game play events. Such models can describe, for instance, a pattern of increasing difficulty, a mix of positive and negative events, or phases of intense action broken up by pauses. The study identifies relevant exemplary contributions in the discourses of game design, design patterns, Dynamic Difficulty Adjustment, tabletop roleplay, literary drama, Flow theory, and Operant Conditioning theory. It is found that surprisingly few models exist. The predominant model appears to be repeated variations of rather simple experiential sequences of tension and relaxation on an overall increasing level, similar to the dramatic arc. The study’s main contribution to the narratology discourse is to show how the dramatic arc as a powerful conceptual model can inform the understanding of the strategic ordering of game play events.

Colonial Korean printshops (1910–1945) operated within severe material constraints that produced apparent typographic standardization. Manual bibliography has nevertheless identified subtle shop-specific signatures in letterforms. This article tests whether computational methods can detect these signatures systematically across large corpora. We apply multiple instance learning to 57,583 page images from four major printshops in Kyŏngsŏng (Seoul). To avoid same-book leakage, we evaluate page classification with all pages from each tested book held out from training. Under this stricter protocol, the model assigns pages to printshops with 84.5 percent accuracy while producing interpretable attention maps. Clustering high-attention patches reveals that the four presses occupy the same regions of the embedding space, reflecting their shared typographic infrastructure. Differentiation emerges through density patterns within this shared space rather than through distinct typographic vocabularies. Each press concentrates disproportionately in clusters capturing specific sub-character features, such as terminal angles, serif weight, and stroke thickness. These computationally identified patterns align with features that De Fremery and Ryu documented through intensive manual analysis of individual volumes. The correspondence supports the approach while extending it to scales that manual bibliography cannot achieve. This framework shows how computational analysis can test and extend humanities scholarship on historical print culture by surfacing the specific visual features that distinguish institutional outputs within materially constrained production systems.

The article offers empirical foundations for machine text classification within the immediacy-distance continuum. By introducing an innovatively stratified collection of German texts from diverse media and communicative situations, we compute prototypical characteristics based on text-internal language properties. The application of a linguistically motivated feature set to supervised machine learning not only enables automatic classification but also produces interpretable explanatory models. Findings, and particularly methodological insights, should be transferable to other languages.

The European Research Council-funded project “STEMMA: Systems of Transmitting Early Modern Manuscript Verse, 1475 to 1700” computationally models the manuscript circulation of early modern poetry to gain macro-level insights into the overlapping contexts in which it was written, read, and copied. To facilitate STEMMA’s ultimate goal of modeling the circulation of manuscript poetry as a large-scale network, the project began by combining and reconciling five of the most authoritative sources of relevant data using staged large language model (LLM)-assisted pipelines. These pipelines were effective on a large proportion of the data, but they struggled in relation to particular features of early modern poetics, including imitation, translation, and unique works. This article considers how general models’ shortcomings in relation to early modern poetry can be considered a feature rather than a bug, pointing up new insights into poetics by defamiliarizing them. It considers productive intersections between literary theory and criticism and recent scholarship on LLMs and suggests that two seemingly very different fields of study address what are, at core, rather similar fundamental questions.

Textual criticism, the task of reconstructing a text as close as possible to its original form based on multiple ancient copies, is a central concern of philologists studying ancient literature. After identifying and cataloging the available manuscripts, scholars align the texts to produce a list of variants, a process known as collation. Automating this task has been one of the earliest areas of interest for applying computational methods to the humanities. Most current approaches rely on sequence alignment algorithms, originally developed for bioinformatics. However, recent advances in monolingual and multilingual neural alignment, capable of modeling semantic, syntactic, and grammatical relationships, remain largely underutilized in this field. In this article, we introduce Alignment and Learning for Manuscript Analysis (ALMA). This new pipeline integrates lexical, grammatical, and semantic analysis to perform automatic textual alignment. We evaluate ALMA against three established alignment methods, each representing a different paradigm, on four manuscripts of the Gospel of John, two in Latin and two in Greek. ALMA achieves mean alignment scores of 94.12 percent (Latin) and 95.77 percent (Greek), significantly outperforming all baseline methods: alignment scores are improved over a deep-learning-only approach by 1.93 percent and 1.09 percent, over a grammar-based approach by 23.39 percent and 10.14 percent, and over a sequence alignment method by 38.57 percent and 13.39 percent, respectively, for Latin and Greek. Notably, in this study, ALMA is the only approach to achieve a median alignment score of 100 percent on both datasets. These results highlight ALMA’s substantial potential for advancing computational approaches for textual collation and more generally for computational philology.

This study uses the Palestinian Oral History Archive to investigate how Palestinian refugees in Lebanon sustain a cohesive collective memory of the Nakba through shared narratives. Grounded in Halbwachs’ theory of collective memory, we employ statistical analysis of narrative pairwise similarities, examining the influence of shared gender and geography. We use textual representations and semantic embeddings to represent the interviews. Our analysis yields robust findings. Shared origin emerges as the primary driver of narrative similarity across thematic, factual, and semantic measures. This foundational connection is significantly amplified by a shared residence in Lebanon, with refugees sharing both origin and residence, exhibiting the highest narrative cohesion. Additionally, women’s narratives show greater thematic cohesion, particularly in recounting experiences of the British occupation, underscoring the gendered dimensions of memory formation. This research deepens understanding of collective memory in diasporic settings, emphasizing the critical role of oral histories in safeguarding Palestinian identity and resisting erasure.

This article studies the afterlife of poems as examples. Using the Princeton Prosody Archive and a large reference corpus of anglophone poetry, we recover likely poem quotations across works on prosody, elocution, grammar, rhetoric, and literary instruction from 1532 to 1929. We treat reception as a practical history of availability: the ways poems were cut into lines, couplets, and stanzas and made to do explanatory work. Methodologically, the article combines text-reuse detection with corpus filtering and hierarchical modeling to distinguish spurious textual overlap from historically plausible quotation and to trace poem-level trajectories through the archive. Those trajectories reveal specific contours of a shift in the repertoire of teachable verse: the Augustan classical-translation apparatus that had organized verse pedagogy through the eighteenth century gave way, across the long nineteenth century, to a Romantic and lyric repertoire whose lines could travel without contextual framing. The resulting account describes the quotable canon as a collection of poems and fragments whose authority was made through use. It shows how poetic reception can be studied at scale while remaining attentive to the instability of quotation, attribution, and reuse.

This article addresses a significant challenge hindering large-scale research into past human social networks: the lack of structured and standardised documentation for published network data, alongside inconsistent community-wide adoption of such practices. Although the community of practitioners applying network analysis to historical and archaeological sources remains small and the corpus of available network datasets is still limited, there is a clear demand for shared infrastructural resources, which are increasingly viewed as essential to sustaining and scaling the field. To overcome these obstacles, the authors propose a formulation of past network data standards that reflect current usage and identify missing components to achieve best practices while adhering to the FAIR and LOUD principles of Open Science. The proposed standards aim to enhance understanding of data provenance, licensing, and feasibility for specific research scenarios, while also promoting reproducibility and facilitating the automation of data reuse for large-scale comparative studies. Additionally, the Past Networks Repository was developed to support the uptake of these common standards and improve resource findability. The repository explicitly implements these data standards to facilitate collaboration and research dissemination.