Volume 1, Issue 1-2

This paper explores the “semiotics of latency,” examining how the discipline of signs can provide insights into the increasingly invisible dimensions of contemporary communication, especially with the rise of digital technologies and artificial intelligence. The study proposes that semiotics has become more relevant today as it can reveal the latent aspects of communication, which have expanded significantly with technological advancements. The discussion spans various themes including invisibility, subtlety, infrastructure, transcendence, magic, sentience, immateriality, and minority, offering a nuanced understanding of how latent spaces – particularly in AI – shape meaning in modern contexts. By tracing historical perspectives from figures like Girolamo Cardano and Robert Hooke to contemporary phenomena like AI-generated content and its societal impacts, the paper argues for a “subtle reading” approach in semiotics. This approach bridges close reading and distant reading methods to decode the complex, often invisible infrastructures that underlie digital communication. The exploration of these hidden spaces suggests new horizons for understanding both the potential and the ethical implications of AI and digital technologies, positioning semiotics as a critical tool for navigating the unseen forces that increasingly influence our lives.

The topic of this paper is a thirteenth-century manuscript from the French National Library (Paris, BnF français 24428) containing three popular texts: an encyclopedic work, a bestiary and a collection of animal fables. We have automatically transcribed the manuscript using a custom handwritten text recognition (HTR) model for old French. Rather than a content-based analysis of the manuscript’s transcription, we adapt quantitative methods normally used for authorship attribution and clustering to the analysis of scribal contribution in the manuscript. Furthermore, we explore the traces that are left when texts are copied, transcribed and/or edited, and the importance of that trace for computational textual analysis with orthographically unstable historical languages. We argue that the method of transcription is fundamental for being able to think about complex modes of authorship which are so important for understanding medieval textual transmission. The paper is inspired by trends in digital scholarship in the mid-2020s, such as public transcribe-a-thons in the GLAM (Galleries, Libraries, Archives and Museums) sector, the opening up of digitized archival collections with methods such as HTR, and computational textual analysis of the transcriptions.

Digital devices have been increasingly integrated into language learning environments, particularly since the COVID-19 pandemic. Existing literature, focusing predominantly on dominant languages like English, presents mixed findings on the effectiveness of digital resources for language learning. Few studies address heritage languages, which often have limited resources beyond the home and may depend more on digital tools for support. This longitudinal, mixed-methods study investigated the impact of digital device use on heritage language learning among Chinese–Canadian families. We examined the relationship between digital device use and Chinese receptive vocabulary among 128 first graders, 137 second graders, and 66 third graders over three years. Additionally, we conducted parental interviews with 42 focal families for three years to explore the evolving patterns of digital resource use at home. Our findings revealed a statistically significant positive impact of digital device use on Chinese receptive vocabulary development among first and second graders, while no significant effects were observed in third graders. The analyses of parental interviews uncovered increased digital use, diversity of resources, positive parental attitudes, and digital literacy among families from grades 1 to 2 but decreased digital use and parental enthusiasm in the third grade due to health and addiction concerns, reinforcing the quantitative results. Conducted during the COVID-19 pandemic, this study offers a unique perspective on how families’ digital device use for heritage languages changed before, during, and after the pandemic. The findings offer valuable insights for families and educators to better support heritage language learners with digital resources.

Recent advances have made Artificial Intelligence/Machine Learning (AI/ML) processes increasingly relevant to business, healthcare, finance, retail, and telecommunications interests. The objective of the present study is to explore the potential to leverage modern machine learning algorithms directly into humanities research fields. To do so, the renAIssance project was created, aiming to examine various possibilities of using AI/ML algorithms for Optical Character Recognition (OCR) to accelerate and improve the accuracy of automatized transcription in digitized historical archival documents. This article considers the state of the field as it pertains to the main processes used in natural language processing. It also explores difficulties arising from salient features of early modern printing practices that diverge from modern typographical conventions. Four AI/ML approaches were employed to achieve context-rich processing of a specifically selected historical archival dataset: Convolutional Neural Networks, Sequence-to-Sequence Contrastive Learning (SeqCLR), Vision Transformers, and Transformer-based OCR (TrOCR). The archival corpus consisted of 931 pages, 2,082 folios, and 61 manual transcriptions as ground truth to train the algorithms. This study reports on the accuracy achieved by each of the four methods when transcribing and transliterating early modern documents. Finally, it offers suggestions for future implementations to apply AI/ML tools to the analysis of archival sources commonly handled by researchers on humanities fields such as literature and history.

This article examines the integration of topic modeling within literary studies, highlighting its potential to transform conventional literary analysis through computational techniques. It reviews the theoretical underpinnings underlying topic modeling, including prominent algorithms like Latent Dirichlet Allocation, Non-negative Matrix Factorization, and Neural Topic Models, and discuss their utility in dissecting large textual corpora to uncover latent thematic and stylistic patterns. The article subsequently addresses the specific methodological steps for effective implementation, spanning text preprocessing model tuning and result interpretation. We further illustrate the diverse applications of topic modeling in literary studies through thematic analysis, comparative studies, and the extraction of cultural and historical insights. Challenges such as model accuracy, technical limitations, and ethical considerations are critically assessed. The review concludes by envisioning prospective future directions that foresee enhanced integration of topic modeling into literary criticism, facilitated by technological and interdisciplinary advancements.

Nigerian literature has long used paper and film as primary media for cultural expression and exporting African culture and civilisation. However, the advent of datafication and botification in the turn of millennium, particularly within social media, has brought forth a new era. This shift is peculiarly evident in the ‘Twitterverse’, where Nigerian Twitterature, or ‘Xerature’, emerged in 2014, largely due to the work of Nigerian-American novelist Teju Cole. His minimalist ‘Twittories’ addressed Nigeria’s socio-political landscape. This article aims to raise awareness, trace the origins, and define this evolving genre while proposing canonisation and methodological approaches to its study reported in a sustained reading of Hafsat Dauda (@HafsahDauda)’s Las, Las Nigeria is Home 2020.

While ChatGPT has received increasing attention in the age of artificial intelligence, little effort has been made to investigate Chinese as a foreign language (CFL) learners’ acceptance of ChatGPT use for Chinese learning. This mixed-method study aims to unpack CFL learners’ intentions and perceptions of leveraging ChatGPT for learning purposes by integrating the technology acceptance model and social cognitive theory. To this end, quantitative data were collected from 120 CFL learners by using questionnaires tapping into perceived ease of use, perceived usefulness, behavioral intention, facilitating conditions, and growth mindset, and analyzed by partial least squares structural equation modeling. Quantitative data were supplemented by qualitative data in the form of learners’ responses to an open-ended question. Quantitative results indicated that perceived usefulness, growth mindset, and facilitating conditions were significant antecedents of learners’ intention to use ChatGPT, while perceived ease of use was not. The qualitative findings revealed students’ varied perspectives on integrating ChatGPT into the Chinese learning process. The study sheds light on the underlying mechanisms of CFL learners’ behavioral intention to use ChatGPT and provides context-specific and nuanced insights into CFL learners’ perceptions of ChatGPT-assisted language learning.

Across numerous languages, individuals frequently employ two spatial perspectives – namely, the Moving Time and Moving Ego metaphors – to represent the movement of events in time. Specifically, the Moving Time perspective conceptualizes time as a dynamic entity that moves toward a stationary observer situated in a fixed location. In contrast, the Moving Ego metaphor positions the individual (the “ego”) as the one traversing through time, while time itself remains static. Previous research has established a positive association between procrastination and the adoption of the Moving Ego perspective. Drawing on findings that highlight the robust link between procrastination and perfectionistic concerns, we hypothesized that perfectionistic tendencies would correlate with a greater propensity to invoke an ego-moving representation of time. The present research tests this hypothesis and provides substantial support for this relationship. We demonstrate that a well-established measure of perfectionistic concerns is linked to an increased use of the Moving Ego perspective when interpreting ambiguous temporal utterances. Furthermore, this relationship is mediated by procrastination. This study presents the first empirical evidence that individual differences in perfectionistic concerns may influence how people perceive and interpret temporal relationships.