Affective disruption: Walter Benjamin and the 'history' of Ireland's industrial schools

What role do organizations play in writing history? In this paper, I address the part played by organizations in the enactment of large-scale violence, and focus on the ways in which the resulting histories come to be written. Drawing on the case of Ireland's industrial schools, I demonstrate how such accounts can act to serve the interests of those in power, effectively silencing and marginalizing weaker people. A theoretical lens that draws on ideas from Walter Benjamin and Judith Butler is helpful in understanding this; the concept of 'affective disruption' enables an exploration of how people's experiences of organizational violence can be reclaimed from the past, and protected in a continuous remembrance. Overall, this paper contributes a new perspective on the writing of organizational histories, particularly in relation to the enactment of violence. 

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.