Papers
. (In Press). Supporting and augmenting human and machine learning in citizen science: Lessons from Gravity Spy. Citizen Science: Theory And Practice.
. (2024). 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
ECSCW2024_Folksonomy_Crowdsourcing__Final_.pdf (1.59 MB). (2024). 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.
TREW_Workshop_Paper_2024.pdf (392.26 KB). (2023). 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
Design_Background_iConf.pdf (3.78 MB). (2021). 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.
Dissertation_MH.pdf (3.92 MB). (2020). 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
RA-JAIS-17-0130.R3.1_FIN to share.pdf (892.03 KB). (2020). 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
Social_Construction_of_ML_in_GS_HICCS2020.pdf (124.3 KB). (2020). 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
transaction paper final figures in text.pdf (1.39 MB) Novelty_Motivator_2020.pdf (1.15 MB). (2020). 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
3392841.pdf (1.13 MB). (2020). 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
MLGT-preprint.pdf (2.43 MB). (2019). 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/
Linguistic Adoption (ICIS) final.pdf (3.07 MB). (2018). 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
JCOM_1701_2018_A02.pdf (306.9 KB). (2018). 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
Quench to distribute.pdf (710.03 KB). (2018). 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
anonymous-contributions-cameraready.pdf (1.25 MB). (2018). 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
ECSCW-Paper-Final.pdf (2.14 MB). (2018). 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
crowston fagnot to distribute.pdf (3.76 MB). (2017). Blending machine and human learning processes. In Hawai'i International Conference on System Sciences. https://doi.org/10.24251/HICSS.2017.009
training v3 to share.pdf (245.57 KB)