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result(s) for
"Modeling Fiction."
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Gray Rabbit's 1, 2, 3
Gray Rabbit finds some clay and makes different kinds of animals, from one wiggly worm to ten scurrying mice.
Book
Imagination extended and embedded
This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter (e.g., Frigg in Synthese 172(2):251–268, 2010; Frigg and Nguyen in The Monist 99(3):225–242, 2016; Godfrey-Smith in Biol Philos 21(5):725–740, 2006; Philos Stud 143(1):101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities are related to actual representational tools, and coordinated among different scientists, and with real-world phenomena. The artifactual account focuses, in contrast, on the culturally established external representational tools that enable, embody, and extend scientific imagination and reasoning. While there are commonalities between models and fictions, it is argued that the focus should be on the fictional uses of models rather than considering models as fictions.
Journal Article
Madame Tussaud's apprentice
In 1789 Paris, Celie Rousseau lives on the streets stealing to survive, but when she is arrested she is given the remarkable opportunity to become an apprentice to Madame Tussaud, and as the Revolution begins, she must choose between her royal patrons and Algernon, the freedom fighter she loves.
BOOK
Traveling with TARDIS. Parameterization and transferability in molecular modeling and simulation
The English language has adopted the word Tardis for something that looks simple from the outside but is much more complicated when inspected from the inside. The word comes from a BBC science fiction series, in which the Tardis is a machine for traveling in time and space, that looks like a phone booth from the outside. This paper claims that simulation models are a Tardis in a way that calls into question their transferability. The argument is developed taking Molecular Modeling and Simulation as an example. There, simulation models are force fields that describe the molecular interactions and that look like simple and highly modular mathematical expressions. To make them work, they contain parameters that are adjusted to match certain data. The role of these parameters and the way they are obtained is seriously under-appreciated. It is constitutive for the model and central for its applicability and performance. Hence, the model is more than it seems so that working with adjustable parameters deeply affects the ontology of simulation models. This is particularly crucial for the transferability of the models: the information on how a model was trained is like luggage the model must carry on its voyage.
Journal Article
The Lure of the Virtual
Although organizational scholars have begun to study virtual work, they have yet to fully grapple with its diversity. We draw on semiotics to distinguish among three types of virtual work (virtual teams, remote control, and simulations) based on what it is that a technology makes virtual and whether work is done
with or
on
,
through
, or
within
representations. Of the three types, simulations have been least studied, yet they have the greatest potential to change work's historically tight coupling to physical objects. Through a case study of an automobile manufacturer, we show how digital simulation technologies prompted a shift from symbolic to iconic representation of vehicle performance. The increasing verisimilitude of iconic simulation models altered workers' dependence on each other and on physical objects, leading management to confound operating
within
representations with operating
with or
on
representations. With this mistaken understanding, and lured by the virtual, managers organized simulation work in virtual teams, thereby distancing workers from the physical referents of their models and making it difficult to empirically validate models. From this case study, we draw implications for the study of virtual work by examining how changes to work organization vary by type of virtual work.
Journal Article
Experimental narratives: A comparison of human crowdsourced storytelling and AI storytelling
The paper proposes a framework that combines behavioral and computational experiments employing fictional prompts as a novel tool for investigating cultural artifacts and social biases in storytelling both by humans and generative AI. The study analyzes 250 stories authored by crowdworkers in June 2019 and 80 stories generated by GPT-3.5 and GPT-4 in March 2023 by merging methods from narratology and inferential statistics. Both crowdworkers and large language models responded to identical prompts about creating and falling in love with an artificial human. The proposed experimental paradigm allows a direct and controlled comparison between human and LLM-generated storytelling. Responses to the Pygmalionesque prompts confirm the pervasive presence of the Pygmalion myth in the collective imaginary of both humans and large language models. All solicited narratives present a scientific or technological pursuit. The analysis reveals that narratives from GPT-3.5 and particularly GPT-4 are more progressive in terms of gender roles and sexuality than those written by humans. While AI narratives with default settings and no additional prompting can occasionally provide innovative plot twists, they offer less imaginative scenarios and rhetoric than human-authored texts. The proposed framework argues that fiction can be used as a window into human and AI-based collective imaginary and social dimensions.
Journal Article
LLM-Guided Weighted Contrastive Learning with Topic-Aware Masking for Efficient Domain Adaptation: A Case Study on Pulp-Era Science Fiction
Domain adaptation of pre-trained language models remains challenging, especially for specialized text collections that include distinct vocabularies and unique semantic structures. Existing contrastive learning methods frequently rely on generic masking techniques and coarse-grained similarity measures, which limit their ability to capture fine-grained, domain-specific linguistic nuances. This paper proposes an enhanced domain adaptation framework by integrating weighted contrastive learning guided by large language model (LLM) feedback and a novel topic-aware masking strategy. Specifically, topic modeling is utilized to systematically identify semantically crucial domain-specific terms, enabling the creation of meaningful contrastive pairs through three targeted masking strategies: single-keyword, multiple-keyword, and partial-keyword masking. Each masked sentence undergoes LLM-guided reconstruction, accompanied by graduated similarity assessments that serve as continuous, fine-grained supervision signals. Experiments conducted on an early 20th-century science fiction corpus demonstrate that the proposed approach consistently outperforms existing baselines, such as SimCSE and DiffCSE, across multiple linguistic probing tasks within the newly introduced SF-ProbeEval benchmark. Furthermore, the proposed method achieves these performance improvements with significantly reduced computational requirements, highlighting its practical applicability for efficient and interpretable adaptation of language models to specialized domains.
Journal Article
Quantitative thematic diversification and evolution of classical science fiction in the public domain based on complex network analysis and natural language processing
Science Fiction (SF) is a young but thriving modern literary genre characterized by vivid portrayals of alternate worlds featuring advanced science and technology, often distant in space and time from ours. Establishing captivating, fantastical, and futuristic environments for imaginative storytelling constantly required profound imagination and innovative thinking of the genre’s creators, propelling the young literary genre to evolve and grow in a relatively short time into one that now exerts a strong influence even outside its original realm of literature, such as cinema and television. As creative works in the written form, the texts of SF novels are the primary source for understanding their nature and characteristics, including the developmental history. In this paper we study in detail how SF has evolved since its beginning via two quantitative techniques, computational linguistics (natural language processing) and network science, jointly applied to a comprehensive data of classical SF in the public domain. We find that the network constructed between the texts based on linguistic similarity enables us to detect the emergence and evolution of distinct themes across different generations, and that the most important events in the history of SF strongly correlate with moments of rapid growth in the genre’s thematic diversity. This shows the necessity of a continuous infusion of new ideas and the resulting elevation in diversity for the success and growth of a creative genre. Also, in this age of a strengthening interest in the scientific understanding of human creativity and machine intelligence, this work represents a contribution to one of the most promising yet underrepresented topics in human-centered data science.
Journal Article
Gender differences in reading medium, time, and text types: Patterns of student reading habits and the relation to reading performance
Reading habits play an active role in promoting students’ reading skills and making them prepared to participate in modern society. Gender differences regarding students’ habits in the use of reading medium, amount of time spent on leisure reading, and the frequency of school-related reading and of leisure reading were examined. The relation between these habits and the level of student reading performance was further explored. Data on 439,847 15-year-old students in 61 countries/regions were extracted from the most recent database of the Programme for International Student Assessment. Descriptive statistics showed that female students preferred print reading and multiformat use, that they spent more time on leisure reading, and that they read fiction and magazines more often than male students. Then, 3-level hierarchical linear modeling was conducted. The results indicated that the use of a paper format, the school-related reading of texts with tables or graphs and of fiction, and the leisure reading of fiction and nonfiction positively influenced reading performance among members of both gender groups and that a small amount of leisure reading of magazines and newspapers only showed a significant, albeit small, positive impact among members of the female group. Additionally, more than 2 h of leisure reading a day brought greater benefits for female students, while 1 to 2 h a day seemed to be more effective for male students. The practical implications for the cultivation of reading habits by students as well as those for the implementation of educational interventions were further discussed.
Journal Article
Models and Fiction
Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of the representational arts. Harvard University Press, Cambridge/MA) has the resources to answer these questions. I introduce this account, outline the answers that it offers, and develop a general picture of scientific modelling based on it.
Journal Article