Schecter Serial Number Database
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In this paper we identify the different configurations of Australia’s harm reduction services and analyse their response to COVID-19. We examine key developments that have occurred for each service since the early days of the pandemic, highlighting a number of key themes that have evolved in response to the evolving risks associated with this new public health threat. In turn, this analysis highlights several key questions for Australian harm reduction services, and identifies a number of important debates for researchers and policy makers.
Like many countries, Australia has moved into a phase of increased contact tracing. This means that researchers and practitioners are tasked with sifting through dense datasets to produce effective analyses of their utility. In the case of Covid-19, this has led to some fascinating insights into the use of social media in the context of public health. Using tweets from Australia, the UK, the US and Italy, researchers have worked in partnership with the research team at Data61 to provide the first set of data-driven analyses of the public response to COVID-19. The results we present in this paper build on the analysis of the English language blogosphere from our previous paper , which uses sentiment analysis to understand public opinion. From here we have used the extended networks that we have been building to collaboratively analyse the trajectories of public opinion and measures taken in all four countries.
In this paper, rather than mapping the statistical relationships between items or topics directly, we develop a graphical approach to the representation of social relationships, based on the intuition that bringing distant concepts closer to one another is an effective way to efficiently model the nuances of our perceptions of the world. This ‘social network model’ is composed of individuals that are connected by weighted links, representing the strength of their connection between concepts. It can be viewed both as an extension of the word–word co-occurrence network considered above to allow for edge weights, and as a form of a rich-get-richer recommendation network, where more frequently co-occurring concepts are connected by stronger edges than less-frequently occurring concepts. The network matrix can be either symmetric (i.e. d2c66b5586