Carsten Østerlund

Members of cooperative groups can work together more effectively if they develop a shared classification schema, but distributed groups face barriers to doing so. To better understand how classifications and classification practices can emerge and support the work of distributed groups, we review the literature on folksonomies (a kind of shared classification schema) in crowdsourcing projects (one type of distributed work). The review yields three potentially productive tensions associated with the development of folksonomies in crowdsourcing projects. First, projects must establish who has the authority to decide on adopted terminology and with what consequences. Second, there can be tension if people who tag objects have different interests in tagging than those who use the tags to search for content. Finally, projects must decide when to intervene to maintain a balance between a stable vocabulary and the ability of the project to accommodate ongoing changes. We illustrate these tensions by comparing how they are handled in the photo-sharing site Flickr, the story-sharing site Archive of Our Own (AO3), the internet culture classification site Know Your Meme, and the citizen science project Gravity Spy. The comparison suggests guidelines for project managers regarding the identified tensions.

We explore patterns of interaction with different learning resources (e.g., forums) to predict learning outcomes in an online citizen science project called Gravity Spy. To explore how volunteers engage with and benefit from these resources, we categorize them based on Sørensen's three forms of presence in learning environments: authority-subject, agent-centered, and communal presence. Methodologically, we apply sequence analysis to traces of volunteer interactions with the project to identify engagement patterns with these resources that predict learning. Our interpretation of these patterns is augmented by insights gleaned from interviews with volunteers about their work and use of learning resources. We find that early in the project, volunteers have only a simple task to learn, and completing that task is most predictive of their learning. At more advanced levels, when tasks become more complex, discussions with other volunteers become increasingly important, and interaction patterns become more varied. Viewing learning as a series of routines allows us to articulate precisely how and in what context learning occurs. We conclude by discussing the implications of these findings for designing citizen science projects that promote learning.

The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine-learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to the architecture's simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Using data from previous observing runs, we evaluate different fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. Our new O4 classifier shows improved performance, and will enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena.

This poster explores linguistic styles and lexical entrainment among participants in a citizen science project. Focusing on how language evolves within the community over time, we analyze lexical trends and participant language adaptation. Lexical entrainment, or the convergence of language styles among users, was assessed to understand its impact on participant retention and community cohesion. Our findings reveal that as volunteers engage with the project, notable shifts in language patterns occur, potentially reflecting changes in their roles and integration into the community. The results demonstrate that sustained engagement correlates with deeper linguistic alignment, focused on enhancing collaborative efforts and knowledge production. These results can inform the design of more effective communication strategies and collaborative tools, ultimately improving the productivity and cohesiveness of collaboration in other open collaboration communities.

We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. The study takes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-learning, where both humans and machines contribute to each other’s learning and capabilities. The study takes its point of departure in the literature on co-learning and develops a framework for designing projects where humans and machines mutually enhance each other’s learning. The research contributes to the existing literature by developing a dynamic approach to human-AI augmentation, by emphasizing that the ZPD supports ongoing learning for volunteers and keeps machine learning aligned with evolving data. The approach offers potential benefits for project scalability, participant engagement, and automation considerations while acknowledging the importance of tutorials, community access, and expert involvement in supporting learning.

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

Artificial intelligence (AI) reconfigures work and organization, while work and organization shape AI. In this special issue, we explore these mutual transformations and how they play out across industries and occupations. We argue that, to truly appreciate this transformative power, the use of AI should be understood in relation to key dimensions of the work context. In this editorial, we discuss the sociotechnical dynamics of AI implementation, the research landscape of AI in the context of work, and key contextual factors on the macro‐ and micro‐level that help understand the AI‐work nexus. We then provide directions for future research at the intersection of work and AI.

The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: fast scattering/crown and low-frequency blips . Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that fast scattering/crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that ∼27% of all transient noise at LIGO Livingston belongs to the fast scattering class, while ∼8% belongs to the low-frequency blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.