Safe Returns: Nostalgia and Television

Derek Kompare’s work on North American television examines what he refers to as the ‘regime of repetition’ — the constant recirculation of the nation’s cultural and individual pasts in the present through the ubiquity of past television (2002, p. 19). His book Rerun Nation (2005) traces the historical development of the rerun on American television, from the industrialisation of culture in the nineteenth century to the emergence of the DVD market in the late 1990s. What clearly emerges in Kompare’s work is the production of a constantly evolving and ‘dynamic television heritage’ (2002, p. 20) creating specific forms of public history and memory, both of television itself and the world it represents. Television is central to our understandings of the past and by paying attention to the recirculation of television’s own past, the devices and forms of re-contextualisation, we can reveal specific attitudes towards television as a cultural form and attitudes towards our historical selves. As Kompare successfully demonstrates, ‘how we — as viewers and scholars — “remember” the television of a particular time is inescapably bound to how television remembers itself’ (2002, p. 31), and how television remembers itself is bound to the construction of broader social and cultural memory. Archive or ‘old’ television, particularly news and current affairs footage, forms the basis of much popular modern history on television. This chapter, however, has a more specific focus in its consideration of ‘television about television’. 1 It is a focus which allows me to consider the complex interplay between the old and new, the past and present that we witness in television programming, as specific televisual structures of and relations to the past can tell us more about television’s own memory cultures and their influence on the construction of broader cultural memories. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.