Julia Bullard

TL;DR: A coding scheme is developed to identify software “mentions” and classify them according to their characteristics and ability to realize the functions of citations, providing recommendations to improve the practice of software citation.

TL;DR: This one- day workshop will be led by academic and industry researchers and sets out to achieve three goals: identify cases and ongoing projects on the topic of participation in algorithmic ecosystems; create a tactical toolkit of key challenges and strategies in this space; and set a forward-facing agenda to provoke further attention to the changing role of Participation in contemporary sociotechnical systems.

TL;DR: This paper uses a user-driven classification system for a large, established, and growing fanfiction collection as an example of a successful project of this type, and reveals strategies that diverge from other HCI research on motivation.

TL;DR: This paper demonstrates how the analysis of daily classification design can illuminate the interaction between disparate philosophies of classification and connects a ubiquitous and observable element of classification design – the application of warrant – to longstanding divisions in classification theory.

TL;DR: This mixed-methods study interviewed 13 HCI researchers, defined a systematic data collection procedure for meta-analysis of participant gender data, and created a participant gender dataset from 1,147 CHI papers, providing empirical evidence for the underrepresentation of women, the invisibility of non-binary participants, deteriorating representation of women in MTurk studies, and characteristics of research topics prone to bias.