Public knowledge beyond journalism : infotainment, satire and Australian television

This thesis examines the changing relationships between television, politics, audiences and the public sphere. Premised on the notion that mediated politics is now understood “in new ways by new voices” (Jones, 2005: 4), and appropriating what McNair (2003) calls a “chaos theory” of journalism sociology, this thesis explores how two different contemporary Australian political television programs (Sunrise and The Chaser’s War on Everything) are viewed, understood, and used by audiences. In analysing these programs from textual, industry and audience perspectives, this thesis argues that journalism has been largely thought about in overly simplistic binary terms which have failed to reflect the reality of audiences’ news consumption patterns. The findings of this thesis suggest that both ‘soft’ infotainment (Sunrise) and ‘frivolous’ satire (The Chaser’s War on Everything) are used by audiences in intricate ways as sources of political information, and thus these TV programs (and those like them) should be seen as legitimate and valuable forms of public knowledge production. It therefore might be more worthwhile for scholars to think about, research and teach journalism in the plural: as a series of complementary or antagonistic journalisms, rather than as a single coherent entity.

Bayesian versus frequentist statistical inference for investi gating a one-off cancer cluster reported to a health department

Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.