Understanding the diverse pathways between science and policy is crucial for assessing the impact of research. In this context, ‘research impact’ refers to the broader societal benefits of scientific research that extend beyond traditional research evaluation metrics. Metrics of academic and scientific excellence, such as h-indexes or bibliometric analyses, often fail to capture the impact of science on policy. To investigate one aspect of how research can influence policy, and therefore probably have impact, this study analysed ice core publications from the Australian Antarctic Program. A dataset of 266 Australian-authored and/or -funded East Antarctic ice core publications was examined, revealing that 123 were cited in 284 international and Australian (domestic) policy and policy-related documents. To analyse the policy influence of the research, key information was categorized into authoring countries, institutions, research themes and the contextual use of publications by the documents. This categorization highlighted the different uses of information and interpretations of ice core publications by separate forums, with some more active compared to passive uses. The ‘Active-Passive’ spectrum developed in this study provides a framework for understanding how research is being used, and it underscores the relevance of ice core science to local Australian governance. The results show that domestic uptake of ice core publications by some Australian states demonstrated more ‘Active’ inclusions in policy to support an area of national interest: water security. The mixed methods piloted in this study can be used to assess scientific publications from national Antarctic programmes with more nuance, thereby offering an improved avenue for measuring research impact.

Optimal control of stochastic bandwidth-sharing networks is typically difficult. In order to facilitate the analysis, deterministic analogues of stochastic bandwidth-sharing networks, the so-called fluid models, are often taken for analysis, as their optimal control can be found more easily. The tracking policy translates the fluid optimal control policy back to a control policy for the stochastic model, so that the fluid optimality can be achieved asymptotically when the stochastic model is scaled properly. In this work we study the efficiency of the tracking policy, that is, how fast the fluid optimality can be achieved in the stochastic model with respect to the scaling parameter. In particular, our result shows that, under certain conditions, the tracking policy can be as efficient as feedback policies.