Democratic governments, owing to limited resources, have no choice but to respond selectively to citizens’ preferences. This study focuses on the characteristic of selective government responsiveness and explores the influencing factors. We argue that institutional and political resources affect selective government responsiveness, and we try to prove this argument through Korea's electronic governance system: the Korean National Petition. Specifically, this article collects and analyzes a unique data set of petitions and government responses in the system between September 2017 and December 2020. The results from multinomial logistic regression showed that government response to petitions differs depending on institutional resources. In addition, in the case of political resources, the influence of the resources on selective responsiveness is different according to incentives to be responsive. Points for practitioners This article reveals that the government shows selective government responsiveness to citizens’ preferences within the electronic governance (e-governance) system according to its resources. This result provides practical lessons for practitioners who are concerned about an e-governance system as a space for communication between the government and citizens. In addition, this article suggests a new direction for scholars by presenting empirical evidence for government responsiveness in governance, which has been primarily conceptually studied because it is difficult to measure directly.
The “Deep 7” bottomfish complex, which consists of six snapper and one grouper species, is a complex that carries high economic and cultural importance to the islands of Hawaii. These bottomfish have been monitored through the National Oceanic and Atmospheric Administration Pacific Islands Fisheries Science Center's Deep 7 fishery‐independent surveys since 2016. These surveys use underwater stereo camera systems that produce hundreds of thousands of images that must be annotated by human analysts in order to generate species‐specific, size‐structured abundance estimates. We developed a citizen science project, called “OceanEYEs,” as a means to effectively process this imagery. A beta test was conducted to determine the accuracy of citizen science annotations in comparison to expert annotators. Our results suggest that aggregated citizen scientist data can achieve accuracy levels approaching that of expert annotators, which has the potential to improve image annotation efficiency and produce large volumes of high‐quality training data to improve machine learning algorithms.
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