Beyond ‘good’ and ‘evil’: breaking down binary oppositions in holocaust representations of ‘privileged’ Jews

In 1986, Auschwitz survivor Primo Levi’s paradigmatic essay entitled ‘The Grey Zone’ highlighted the complex and sensitive issue of so-called ‘privileged’ Jews, an issue that remains at the margins of popular and academic discourse on the Holocaust. ‘Privileged’ Jews include those prisoners in the Nazi concentration camps and ghettos who held positions that gave them access to material and other benefits whilst compelling them to act in ways that have been judged both self-serving and harmful to fellow inmates. The unprecedented ethical dilemmas that confronted ‘privileged’ Jews may be viewed as exemplifying the ‘limit’ events or experiences that were characteristic of the Holocaust, situating them at the threshold of representation, understanding and judgement. Levi’s essay singles out history and film as particularly predisposed to a simplifying trend he identifies – the ‘Manichean tendency which shuns half-tints and complexities,’ and resorts to the black-and-white binary opposition(s) of ‘friend’ and ‘enemy,’‘good’ and ‘evil.’ In the case of ‘privileged’ Jews in particular, such binary oppositions would appear to be inadequate. Adopting an interdisciplinary approach that incorporates the fields of history, philosophy and literature, this paper analyses representations of ‘privileged’ Jews, particularly those prisoners of the Sonderkommandos who were forced to work in the crematoria. The paper demonstrates how easily the boundary Levi maps out for moral judgement can be crossed. It is shown that while Levi suggests judgement should be suspended when confronted with the experiences of victims in extremis, moral evaluations of ‘privileged’ Jews permeate discussions and representations of the Holocaust. When confronted with such emotionally and morally freighted issues, judgement may itself be seen as a ‘limit of representation.’

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.

Social capital and hygiene practices among the extreme poor in rural Bangladesh

We investigate the effect of social capital on hygiene practices pertaining to lives of the extreme poor in rural Bangladesh. Analysing a unique survey dataset for 5,600 extreme poor households, we document a significant positive effect of social capital on sanitary latrine use and wearing shoes/sandals at home for hygiene. We account for the endogeneity of social capital by instrumental variable estimation. Our findings emphasise the role of social capital in preventing common diseases through improving hygiene practices for the extreme poor, who usually lack access to medical services in the event of illness, which has important policy implications.