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1. Crowdsourcing Perceptions of Gerrymandering

   https://doi.org/10.1609/hcomp.v10i1.21993  

   Kelly, Benjamin; Kang, Inwon; Xia, Lirong (October 2022, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   Gerrymandering is the manipulation of redistricting to influence the results of a set of elections for local representatives. Gerrymandering has the potential to drastically swing power in legislative bodies even with no change in a population’s political views. Identifying gerrymandering and measuring fairness using metrics of proposed district plans is a topic of current research, but there is less work on how such plans will be perceived by voters. Gathering data on such perceptions presents several challenges such as the ambiguous definitions of ‘fair’ and the complexity of real world geography and district plans. We present a dataset collected from an online crowdsourcing platform on a survey asking respondents to mark which of two maps of equal population distribution but different districts appear more ‘fair’ and the reasoning for their decision. We performed preliminary analysis on this data and identified which of several commonly suggested metrics are most predictive of the responses. We found that the maximum perimeter of any district was the most predictive metric, especially with participants who reported that they made their decision based on the shape of the districts. 

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2. Practitioner Perceptions of Ansible Test Smells

   https://doi.org/10.1109/ICSA-C57050.2023.00074  

   Zhang, Yue; Wu, Fan; Rahman, Akond (April 2023, 023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C))

   The practice of infrastructure as code (IaC) recommends automated management of computing infrastructure with application of quality assurance, such as linting and testing. To that end, researchers recently have investigated quality concerns in IaC test manifests by deriving a catalog of test smells. The relevance of the identified smells need to be quantified by obtaining feedback from practitioners. Such feedback can help the IaC community understand if smells have relevance amongst practitioners, and derive future research directions. We survey 30 practitioners to assess the relevance of three Ansible test smell categories namely, assertion roulette, local only testing, and remote mystery guest. We observe local only testing to be the most agreed upon test smell category, whereas, assertion roulette is the least agreed upon test smell category. Our findings provide a nuanced perspective of test smells for IaC, and lays the groundwork for future research. 

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3. Children's perceptions of social resource inequality

   https://doi.org/10.1016/j.appdev.2016.11.006  

   Elenbaas, Laura; Killen, Melanie (January 2017, Journal of Applied Developmental Psychology)
4. User Perceptions of Five-Word Passwords

   https://doi.org/10.1145/3564625.3567981  

   Wu, Xiaoyuan; Munyendo, Collins W.; Cosic, Eddie; Flynn, Genevieve A.; Legault, Olivia; Aviv, Adam J. (December 2022, Proceedings of the 38th Annual Computer Security Applications Conference)
5. “Participant” Perceptions of Twitter Research Ethics

   https://doi.org/10.1177/2056305118763366  

   Fiesler, Casey; Proferes, Nicholas (January 2018, Social Media + Society)