  Science and technology journalists today face challenges in finding
newsworthy leads due to increased workloads, reduced resources, and expanding
scientific publishing ecosystems. Given this context, we explore computational
methods to aid these journalists' news discovery in terms of time-efficiency
and agency. In particular, we prototyped three computational information
subsidies into an interactive tool that we used as a probe to better understand
how such a tool may offer utility or more broadly shape the practices of
professional science journalists. Our findings highlight central considerations
around science journalists' agency, context, and responsibilities that such
tools can influence and could account for in design. Based on this, we suggest
design opportunities for greater and longer-term user agency; incorporating
contextual, personal and collaborative notions of newsworthiness; and
leveraging flexible interfaces and generative models. Overall, our findings
contribute a richer view of the sociotechnical system around computational news
discovery tools, and suggest ways to improve such tools to better support the
practices of science journalists.
