Papers
Supporting and augmenting human and machine learning in citizen science: Lessons from Gravity Spy. Citizen Science: Theory And Practice.
. (In Press). Folksonomies in crowdsourcing platforms: Three tensions associated with the development of shared language in distributed groups. In The European Conference on Computer-Supported Cooperative Work (ECSCW). https://doi.org/10.48340/ecscw2024_n06
. (2024). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Gravity Spy: Lessons Learned and a Path Forward. European Physical Journal Plus, 139, Article 100. https://doi.org/10.1140/epjp/s13360-023-04795-4
. (2024). Mutual learning in human-AI interaction. In Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference. Presented at the Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference, Honolulu, HI.
. (2024). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications. Classical And Quantum Gravity, 40(6). https://doi.org/10.1088/1361-6382/acb633
. (2023). Design principles for background knowledge to enhance learning in citizen science. In Information for a Better World: Normality, Virtuality, Physicality, Inclusivity: 18th International Conference, iConference (pp. 563–580). https://doi.org/10.1007/978-3-031-28032-0_43
. (2023). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning. Classical And Quantum Gravity, 38(19). https://doi.org/10.1088/1361-6382/ac1ccb
. (2021). Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research. In A Computing Community Consortium (CCC) Quadrennial Paper. Retrieved de https://cra.org/ccc/wp-content/uploads/sites/2/2021/03/CCC-TransitionPaperImagine-All-the-People.pdf
. (2021). Occasional Groups in Crowdsourcing Platforms. In School of Information Studies. Syracuse University, Syracuse, NY, USA.
. (2021). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Building an apparatus: Refractive, reflective and diffractive readings of trace data. Journal Of The Association For Information Systems, 21(1), Article 10. https://doi.org/10.17705/1jais.00590
. (2020). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.719
. (2020). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Knowledge Tracing to Model Learning in Online Citizen Science Projects. Ieee Transactions On Learning Technologies, 13, 123-134. https://doi.org/10.1109/TLT.2019.2936480
. (2020). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
. (2020).
Novelty_Motivator_2020.pdf (1.15 MB)
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Shifting forms of Engagement: Volunteer Learning in Online Citizen Science. Proceedings Of The Acm On Human-Computer Interaction, (CSCW), 36. https://doi.org/10.1145/3392841
. (2020). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training. Computers In Human Behavior, 105, 106198. https://doi.org/10.1016/j.chb.2019.106198
. (2020). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Characterizing Novelty as a Motivator in Online Citizen Science (Syracuse University). In School of Information Studies. Retrieved de https://surface.syr.edu/etd/1046/
. (2019). Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning. Physical Review D, 99(8), 082002. https://doi.org/10.1103/PhysRevD.99.082002
. (2019). Coordinating Advanced Crowd Work: Extending Citizen Science. Citizen Science: Theory And Practice, 4, 1–12. https://doi.org/10.5334/cstp.166
. (2019). Linguistic adoption in online citizen science: A structurational perspective. In International Conference on Information Systems. Retrieved de https://aisel.aisnet.org /icis2019/crowds_social/crowds_social/28/
. (2019). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Appealing to different motivations in a message to recruit citizen scientists: results of a field experiment. Journal Of Science Communication, 17. https://doi.org/10.22323/2.17010202
. (2018). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Coordinating advanced crowd work: Extending citizen science. In Hawai'i International Conference on System Sciences (51st ed.). https://doi.org/10.24251/HICSS.2018.212
. (2018). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Did they login? Patterns of anonymous contributions to online communities. Proceedings Of The Acm On Human-Computer Interaction, 2(CSCW), Article 77. https://doi.org/10.1145/3274346
. (2018). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Folksonomies to support coordination and coordination of folksonomies. Computer Supported Cooperative Work, 27(3–6), 647–678. https://doi.org/10.1007/s10606-018-9327-z
. (2018). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Stages of motivation for contributing user-generated content: A theory and empirical test. International Journal Of Human-Computer Studies, 109, 89-101. https://doi.org/10.1016/j.ijhcs.2017.08.005
. (2018). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Blending machine and human learning processes. In Hawai'i International Conference on System Sciences. https://doi.org/10.24251/HICSS.2017.009
. (2017). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)