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Through a study of a coalition to promote community land trusts in New York City, this article asks how collective learning unfolds in the context of activism against gentrification and displacement. Drawing on Cultural-Historical Activity Theory (CHAT), we illustrate how the coalition develops as it confronts the contradictory nature of commodified land and housing and navigates the contradictions and other challenges entailed in the process of commoning. Understanding this as a learning process is critical to understanding the politics of urban commoning practice and of particular approaches to it such as community land trusts (CLTs). © 2020, Okanagan University College. ; Peer reviewed

AbstractCombining machine learning with social network analysis (SNA) can leverage vast amounts of social media data to better respond to crises. We present a case study using Twitter data from the March 2019 Nebraska floods in the United States, which caused over $1 billion in damage in the state and widespread evacuations of residents. We use a subset of machine learning, deep learning (DL), to classify text content of 11,982 tweets, and we integrate that with SNA to understand the structure of tweet interactions. Our DL approach pre‐trains our model with a DL language technique, BERT, and then trains the model using the standard training dataset to sort a dataset of tweets into classes tailored to crisis events. Several performance measures demonstrate that our two‐tiered trained model improves domain adaptation and generalization across different extreme weather event types. This approach identifies the role of Twitter during the damage containment stage of the flood. Our SNA identifies accounts that function as primary sources of information on Twitter. Together, these two approaches help crisis managers filter large volumes of data and overcome challenges faced by simple statistical models and other computational techniques to provide useful information during crises like flooding.