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1. A model of students’ conceptions of equivalence.

   Wladis, C.; Offenholley, K.; Sencindiver, B. (July 2021, Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education)

   Inprasitha, Maitree; Changsri, Narumon; Boonsena, Nisakorn (Ed.)

   In mathematics education, much research has focused on studying how students think about the equals sign, but equality is just one example of the larger concept of equivalence, which occurs extensively throughout the K-16 mathematics curriculum. Yet research on how students think about broader notions of equivalence is limited. We present a model of students’ thinking that is informed by Sfard’s theories of the Genesis of Mathematical Objects, in which she distinguishes between operational versus structural thinking (e.g., 1995), which we conceptualize as a continuum rather than a binary categorization. Sfard also describes a pseudostructural conception, in which the objects that a student conceptualizes are not the reification of a process. We combine Sfard’s theory with a categorization of the source of students’ definitions, where stipulated definitions are given a priori and can be explicitly consulted when determining whether something fits the definitions, while extracted definitions are constructed from repeated observation of usage (Edwards & Ward, 2004). We combine these theories with inductive coding of data (open-ended questions, multiple-choice questions, and cognitive interviews) collected from thousands of students enrolled in a range of mathematics classes in college in the US, to generate categories of students’ thinking around equivalence. We see this model as a tool for analysing students’ work to better understand how students conceptualize equivalence. With this model we hope to begin a conversation about how students tend to conceptualize equivalence at various levels, as well as the ways in which equivalence is or is not explicitly addressed currently in curricula and instruction, and what consequences this might have for students’ conceptions of equivalence. 

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2. What Makes People Join Conspiracy Communities?: Role of Social Factors in Conspiracy Engagement

   https://doi.org/10.1145/3432922  

   Phadke, Shruti; Samory, Mattia; Mitra, Tanushree (January 2021, Proceedings of the ACM on Human-Computer Interaction)

   null (Ed.)

   Widespread conspiracy theories, like those motivating anti-vaccination attitudes or climate change denial, propel collective action, and bear society-wide consequences. Yet, empirical research has largely studied conspiracy theory adoption as an individual pursuit, rather than as a socially mediated process. What makes users join communities endorsing and spreading conspiracy theories? We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities. Using a quasi-experimental approach, we first identify 30K future conspiracists?(FC) and30K matched non-conspiracists?(NC). We then provide empirical evidence of the importance of social factors across six dimensions relative to the individual factors by analyzing 6 million Reddit comments and posts. Specifically, in social factors, we find that dyadic interactions with members of the conspiracy communities and marginalization outside of the conspiracy communities are the most important social precursors to conspiracy joining-even outperforming individual factor baselines. Our results offer quantitative backing to understand social processes and echo chamber effects in conspiratorial engagement, with important implications for democratic institutions and online communities. 

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3. We the swarm—Methodological, theoretical, and societal (r)evolutions in collective decision-making research

   https://doi.org/10.1177/26339137221133400  

   Garnier, Simon; Moussaïd, Mehdi (October 2022, Collective Intelligence)

   Collective decision-making constitutes a core function of social systems and is, therefore, a central tenet of collective intelligence research. From fish schools to human crowds, we start by interrogating ourselves about the very definition of collective decision-making and the scope of the scientific research that falls under it. We then summarize its history through the lenses of social choice theory and swarm intelligence and their accelerating collaboration over the past 20 or so years. Finally, we offer our perspective on the future of collective decision-making research in 3 mutually inclusive directions. We argue (1) that the possibility to collect data of a new nature, including fine-grain tracking information, virtual reality, and brain imaging inputs, will enable a direct link between plastic individual cognitive processes and the ontogeny of collective behaviors; (2) that current theoretical frameworks are not well suited to describe the long-term consequences of individual plasticity on collective decision-making processes and that, therefore, new formalisms are necessary; and finally (3) that applying the results of collective decision-making research to real-world situations will require the development of practical tools, the implementation of monitoring processes that respect civil liberties, and, possibly, government regulations of social interventions by public and private actors. 

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4. How Do We Measure Trust in Visual Data Communication?

   https://doi.org/10.1109/BELIV57783.2022.00014  

   Elhamdadi, Hamza; Gaba, Aimen; Kim, Yea-Seul; Xiong, Cindy (November 2022, 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV))

   Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers’ trust in visualizations, visualization designers can leverage design techniques to create visualizations that evoke a "calibrated trust," at which readers arrive after critically evaluating the information presented. To systematically understand what drives readers to engage in "calibrated trust," we must first equip ourselves with reliable and valid methods for measuring trust. Computer science and data visualization researchers have not yet reached a consensus on a trust definition or metric, which are essential to building a comprehensive trust model in human-data interaction. On the other hand, social scientists and behavioral economists have developed and perfected metrics that can measure generalized and interpersonal trust, which the visualization community can reference, modify, and adapt for our needs. In this paper, we gather existing methods for evaluating trust from other disciplines and discuss how we might use them to measure, define, and model trust in data visualization research. Specifically, we discuss quantitative surveys from social sciences, trust games from behavioral economics, measuring trust through measuring belief updating, and measuring trust through perceptual methods. We assess the potential issues with these methods and consider how we can systematically apply them to visualization research. 

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5. How should we promote transient diversity in science?

   https://doi.org/10.1007/s11229-023-04037-1  

   Wu, Jingyi; O’Connor, Cailin (January 2023, Synthese)

   Abstract Diversity of practice is widely recognized as crucial to scientific progress. If all scientists perform the same tests in their research, they might miss important insights that other tests would yield. If all scientists adhere to the same theories, they might fail to explore other options which, in turn, might be superior. But the mechanisms that lead to this sort of diversity can also generate epistemic harms when scientific communities fail to reach swift consensus on successful theories. In this paper, we draw on extant literature using network models to investigate diversity in science. We evaluate different mechanisms from the modeling literature that can promote transient diversity of practice, keeping in mind ethical and practical constraints posed by real epistemic communities. We ask: what are the best ways to promote an appropriate amount of diversity of practice in scientific communities? 

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