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1. Who Will They Choose?: Avatar Selection Without a Gender-Race-Match

   https://doi.org/10.1109/VRW66409.2025.00375  

   Guth, Alice; Peck, Tabitha C (March 2025, IEEE)
2. Who will encourage a sustainable diet? Understanding the psychological predictors of relational organizing

   https://doi.org/10.1007/s11625-023-01390-5  

   Jones, Megan S.; Sekar, Samantha; Dillard, Courtney; Mertens, Andrew; Sparkman, Gregg (August 2023, Sustainability Science)

   Abstract Why do (or do not) people encourage others in their social networks to adopt climate-friendly behaviors? Encouragement like this has been referred to as “relational organizing,” and can help scale up climate action across communities. Since relational organizing is a social behavior, it likely has its own specific barriers and motivations beyond what affects personal climate action. Food is a big part of our day-to-day lives and our relationships with people we care about. It also impacts the climate, ecosystems, animal welfare, and our own health. As such, people’s climate-friendly food choices provide an ideal case study to explore drivers of relational organizing. Using an online survey with two North American samples of motivated audiences (one US-wide animal advocacy community,N = 1166, and one environmentally focused community in Boulder, Colorado,N = 363), we sought to identify and categorize the social–psychological barriers to and drivers of relational organizing for climate-friendly food choices. Using exploratory factor analysis and predictive models, we found that self-efficacy in carrying out the personal behavior, response efficacy beliefs, supportive social norms, and personal aptitude in relational organizing (e.g., personal norms) predicted relational organizing action after the survey. People’s sense of personal obligation to engage in relational organizing (i.e., personal norms), beliefs that would make a difference to important causes (i.e. response efficacy), and social identity beliefs around activism were particularly important. We discuss how these findings can help inform interventions related to climate-friendly diets, and what this means more broadly for how relational organizing can support climate action. 

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3. From Personal Tool to Community Resource: What's the Extra Work and Who Will Do It?

   https://doi.org/10.1145/2675133.2675172  

   Trainer, Erik H.; Chaihirunkarn, Chalalai; Kalyanasundaram, Arun; Herbsleb, James D. (January 2015, Conference on Computer Supported Cooperative Work and Social Computing)

   Sharing scientific data, software, and instruments is becoming increasingly common as science moves toward large-scale, distributed collaborations. Sharing these resources requires extra work to make them generally useful. Although we know much about the extra work associated with sharing data, we know little about the work associated with sharing contributions to software, even though software is of vital importance to nearly every scientific result. This paper presents a qualitative, interview-based study of the extra work that developers and end users of scientific software undertake. Our findings indicate that they conduct a rich set of extra work around community management, code maintenance, education and training, developer-user interaction, and foreseeing user needs. We identify several conditions under which they are likely to do this work, as well as design principles that can facilitate it. Our results have important implications for future empirical studies as well as funding policy. 

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4. Who Will Win: Induced Pluripotent Stem Cells Versus Embryonic Stem Cells for β Cell Replacement and Diabetes Disease Modeling?

   https://doi.org/10.1007/s11892-018-1109-y  

   Jacobson, Elena F.; Tzanakakis, Emmanuel S. (December 2018, Current Diabetes Reports)
5. Who Leads and Who Follows in Strategic Classification?

   Zrnic, Tijana; Mazumdar, Eric; Sastry, Shankar; Jordan, Michael I (July 2021, ArXivorg)

   null (Ed.)

   As predictive models are deployed into the real world, they must increasingly contend with strategic behavior. A growing body of work on strategic classification treats this problem as a Stackelberg game: the decision-maker "leads" in the game by deploying a model, and the strategic agents "follow" by playing their best response to the deployed model. Importantly, in this framing, the burden of learning is placed solely on the decision-maker, while the agents' best responses are implicitly treated as instantaneous. In this work, we argue that the order of play in strategic classification is fundamentally determined by the relative frequencies at which the decision-maker and the agents adapt to each other's actions. In particular, by generalizing the standard model to allow both players to learn over time, we show that a decision-maker that makes updates faster than the agents can reverse the order of play, meaning that the agents lead and the decision-maker follows. We observe in standard learning settings that such a role reversal can be desirable for both the decision-maker and the strategic agents. Finally, we show that a decision-maker with the freedom to choose their update frequency can induce learning dynamics that converge to Stackelberg equilibria with either order of play. 

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