@proceedings {bullard2024, title = {Folksonomies in crowdsourcing platforms: Three tensions associated with the development of shared language in distributed groups}, year = {2024}, abstract = {

Members of cooperative groups can work together more effectively if they develop a shared language, but distributed groups face barriers to doing so. To better understand how shared language can emerge in and support the work of distributed groups, we review the literature on folksonomies (a kind of shared language) in crowdsourcing systems (one type of distributed work). The review highlights three tensions associated with the development of folksonomies in crowdsourcing. The first problem is who has the power to decide on adopted terminology. Second, different users of the language may have different needs. There might be tension if people tagging objects are not the same as those using these tags to search for content. Finally, projects need to decide when to intervene to maintain a balance between a stable ontology and the ability of the project to accommodate ongoing changes. We illustrate these considerations by comparing how they are handled in the story-sharing site Archive of Our Own, the citizen-science project GravitySpy, and the photo-sharing site Flickr.

}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/ECSCW2024_Folksonomy_Crowdsourcing__Final_.pdf}, author = {Bullard, Julia and Crowston, Kevin and Jackson, Corey B. and Smith, Alexander O. and {\O}sterlund, Carsten} } @conference {osterlund2024, title = {Mutual learning in human-AI interaction}, booktitle = {Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference}, year = {2024}, address = {Honolulu, HI}, abstract = {

We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the Zone of Proximal Development concept, which allows us to describe the augmentation of human learning by AI, human augmentation of machine learning and how tasks can be designed to facilitate co-augmentation. Methodologically, the study utilizes 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-augmentation, where both humans and machines contribute to each other{\textquoteright}s learning and capabilities.\  The research contributes to the existing literature by emphasizing the role of ZPD in citizen science projects, showcasing how the concept supports ongoing learning for volunteers and keeps machine learning aligned with evolving data.

}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/TREW_Workshop_Paper.pdf}, author = {{\O}sterlund, Carsten and Crowston, Kevin and Jackson, Casey B. and Takou-Ayaoh, Marlene and Wu, Yunan and Katsaggelos, Aggelos K.} } @proceedings {petridis2023anglekindling, title = {AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models}, year = {2023}, doi = {10.1145/3544548.3580907}, url = {https://savvaspetridis.github.io/papers/anglekindling.pdf}, author = {Petridis, Savvas and Diakopoulos, Nicholas and Crowston, Kevin and Hansen, Mark and Henderson, Keren and Jastrzebski, Stan and Nickerson, Jefrey V and Chilton, Lydia B} } @article {2023, title = {Perceived benefits of open data are improving but scientists still lack resources, skills, and rewards}, journal = {Humanities and Social Sciences Communications}, volume = {10}, year = {2023}, month = {Jan-12-2023}, doi = {10.1057/s41599-023-01831-7}, author = {Borycz, Joshua and Olendorf, Robert and Specht, Alison and Grant, Bruce and Crowston, Kevin and Tenopir, Carol and Allard, Suzie and Rice, Natalie M. and Hu, Rachael and Sandusky, Robert J.} } @article {2021, title = {Hybrid intelligence in business networks}, journal = {Electronic Markets}, year = {2021}, month = {Nov-06-2021}, issn = {1019-6781}, doi = {10.1007/s12525-021-00481-4}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf}, author = {Ebel, Philipp and S{\"o}llner, Matthias and Leimeister, Jan Marco and Crowston, Kevin and de Vreede, Gert-Jan} } @article {2020, title = {GitLab: Work where you want, when you want}, journal = {Journal of Organizational Design}, volume = {9}, year = {2020}, month = {2020/11/16}, pages = {23}, abstract = {

GitLab is a software company that works {\textquotedblleft}all remote{\textquotedblright} at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath.

}, isbn = {2245-408X}, doi = {10.1186/s41469-020-00087-8}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/s41469-020-00087-8.pdf}, author = {Choudhury, Prithwiraj and Crowston, Kevin and Dahlander, Linus and Minervini, Marco S. and Raghuram, Sumita} } @inbook {2019, title = {Comprehensive collaboration plans: Practical considerations spanning across individual collaborators to institutional supports}, booktitle = {Strategies for Team Science Success}, year = {2019}, pages = {587-611}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {This chapter provides a framework for integrating and applying the principles and strategies for effective team science that are described in this volume. The framework, called Collaboration Planning, aims to guide a deliberative approach to assess and plan for ten key influences on both scientific and collaborative success. These influences range from the initial scientific rationale for a team science approach to the collaboration readiness of participating individuals and institutions to team communication and coordination mechanisms to quality improvement for team functioning. The Collaboration Planning framework guides current or future collaborators through dialogue and planning around each influence. It draws their attention to key issues for consideration related to each influence, and facilitates discussion of how to leverage facilitating factors and plan for, or mitigate, challenges. Decisions are captured in a resulting written document called the Collaboration Plan. The Collaboration Plan summarizes the various ways the team plans to build the foundation for, and support, effective collaboration across the lifespan of the team science initiative. Collaboration Plans can be used in multiple ways. The Plans{\textquoteright} core function is as a roadmap to facilitate effective team formation and functioning. The Plan also can be used for benchmarking or guiding quality improvement-oriented evaluation. Collaboration Plans also can be used to communicate a team{\textquoteright}s likelihood of collaborative success, goals, and needs to a wide variety of audiences, including funders, current and future team members, stakeholders in the team{\textquoteright}s success, and organizational leaders. In addition, they can be used as models to guide future teams in laying the foundation for success.}, isbn = {978-3-030-20990-2}, doi = {10.1007/978-3-030-20992-6_45}, author = {Hall, Kara L. and Vogel, Amanda L. and Crowston, Kevin}, editor = {Hall, Kara L. and Vogel, Amanda L. and Croyle, Robert T.} } @inbook {2017, title = {Lessons Learned from a Decade of FLOSS Data Collection}, booktitle = {Big Data Factories}, year = {2017}, pages = {79 - 100}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-59185-8}, issn = {2509-9574}, doi = {10.1007/978-3-319-59186-5}, url = {http://link.springer.com/content/pdf/10.1007/978-3-319-59186-5}, author = {Crowston, Kevin and Squire, Megan}, editor = {Matei, Sorin Adam and Jullien, Nicolas and Goggins, Sean P.} }