Journalistic practices of science popularization in the context of users’ agenda: A case study of „New Scientist”

The article includes a discussion of two models which describe contemporary communication processes in journalism: agenda-setting and news value, indicating the need to expand their research tools to include qualitative methods, and merging the analyses of the reception and the message. It also includes indications as to the possibility, or even the social relevance, of the methods for applying those research perspectives to analysing journalism popularising science. Later, I present the results of an analysis of the content of a sample of 500 most read popular science texts available on the New Scientist website. I demonstrate which thematic areas were valued by the readers, and what values are most commonly applied. Further, upon applying a filter in the form of surveys regarding reader preferences, I discuss the main linguistic devices utilised for controlling readers’ attention. The shaping of the hierarchy of importance of items of news is the result of a dynamic interaction between (1) the thematic priorities and discursive strategies of imposing elite representations of science within media agenda, and (2) the means of negotiating order and values of specific content, which are correlated with readers’ preferences, both in terms of the content and the form of providing popular scientific information

Pair Analytics in a Visual Analytics Context

This case study details the development of “pair analytics” as practical approach to applied analysis and as a scientific research method. The hybrid research project itself was part of a larger research program approved for the Canadian government for their offset program and supported by Federal and Provincial research internships. As a real-world analysis approach, the pair analysis sessions conduced actionable causal chain analysis of aircraft safety. As a scientific method, pair analytics advanced our knowledge of the cognitive science of interpersonal communication in Joint Activities. The paper describes how aerospace researchers and cognitive scientists were able to design a research approach that met constraints from both areas. It concludes with discussion of the implications of this work for highly integrated basic and responsive research in other areas of visualization and analytics

Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detection

Large language models (LLMs) have gained popularity in various fields for their exceptional capability of generating human-like text. Their potential misuse has raised social concerns about plagiarism in academic contexts. However, effective artificial scientific text detection is a non-trivial task due to several challenges, including 1) the lack of a clear understanding of the differences between machine-generated and human-written scientific text, 2) the poor generalization performance of existing methods caused by out-of-distribution issues, and 3) the limited support for human-machine collaboration with sufficient interpretability during the detection process. In this paper, we first identify the critical distinctions between machine-generated and human-written scientific text through a quantitative experiment. Then, we propose a mixed-initiative workflow that combines human experts' prior knowledge with machine intelligence, along with a visual analytics prototype to facilitate efficient and trustworthy scientific text detection. Finally, we demonstrate the effectiveness of our approach through two case studies and a controlled user study with proficient researchers. We also provide design implications for interactive artificial text detection tools in high-stakes decision-making scenarios

The future of historical sociolinguistics?

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