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   Meyer, Michael F; Harlan, Merritt E; Hensley, Robert T; Zhan, Qing; Barbosa, Carolina C; Börekçi, Nahit S; Borrelli, Jonathan J; Bucak, Tuba; Cramer, Alli N; Feldbauer, Johannes; et al (February 2024, Limnology and Oceanography Bulletin)
2. The AEMON‐J “Hacking Limnology” Workshop Series & Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research

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   Meyer, Michael F.; Ladwig, Robert; Mesman, Jorrit P.; Oleksy, Isabella A.; Barbosa, Carolina C.; Cawley, Kaelin M.; Cramer, Alli N.; Feldbauer, Johannes; Tran, Patricia Q.; Zwart, Jacob A.; et al (November 2021, Limnology and Oceanography Bulletin)
3. The correlation between eBird community science and weather surveillance radar‐based estimates of migration phenology

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4. Elementary teachers' verbal supports of science and engineering practices in an NGSS‐aligned science, engineering, and computational thinking unit

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5. Illuminating the collective learning continuum in the Colorado River Basin Science‐Policy Forums

   https://doi.org/10.1002/eet.2125  

   Hovis, Meredith; Gerlak, Andrea K; Heikkila, Tanya; Rittelmeyer, Pam; Koebele, Elizabeth; Méndez‐Barrientos, Linda Estelí; Lubell, Mark (February 2025, Environmental Policy and Governance)

   Abstract Although considerable research over the past two decades has examined collective learning in environmental governance, much of this scholarship has focused on cases where learning occurred, limiting our understanding of the drivers and barriers to learning. To advance knowledge of what we call the “collective learning continuum,” we compare cases of learning to cases where learning was not found to occur or its effects were “blocked.” Through semi‐structured interviews with key stakeholders in science‐policy forums in the Colorado River Basin, a large and complex river basin in western North America, we examine differences and patterns that explain moments of learning, blocked learning, or non‐learning, drawing insights from the collective learning framework. Our results find various factors that influence learning, blocked learning, and non‐learning. We discover technical and social factors as common drivers of both learning and blocked learning. In contrast, we find more structural factors associated with non‐learning. At the same time, the cases reveal insights about the role of political factors, such as timing, legal constraints, and priorities, which are underdeveloped in the collective learning framework. Overall, these findings advance theoretical knowledge of the collective learning continuum and offer practical insights that may strengthen the coordination of science and management for effective governance within the Basin. 

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