Mizrahi Memoirs: History, Memory, and Identity in Displacement

In this dissertation I analyse the dynamics of history, memory, and identity as represented in the published English-language memoirs of Mizrahim (also known as ‘Middle Eastern Jews’ or ‘Arabic Jews’) who were displaced during the mid- to later-twentieth century from Iraq, Iran, and Egypt. I take a thematic approach, analysing the memoirs through a focus on metaphor, sensescapes, dreams, urban landscapes and sacred sites, as well as the different perspectives of key stakeholders. I demonstrate that the culture wars model is inadequate for the study of the experiences of displacement and dispersal. Rather, I argue that the framework of multidirectional memory (Michael Rothberg), in combination with the notion of screen memory, provides a far more accurate reflection of the memory dynamics represented across this body of texts. I also draw on the concepts of postmemory (Marianne Hirsch) and the ‘off-modern’ (Svetlana Boym) as productive ways of understanding the intergenerational transmission of histories and memories, and the construction of diverse identities in post-displacement life. Furthermore, I show that memory dynamics are multidimensional and are shaped by the senses, emotions, and spirituality. They are multilayered, encompassing diverse experiences of temporality, place, and ontology. They are also highly entangled and interweave different perspectives, power relations, locations, histories, and peoples. Through examining the dynamics of memories, histories, and identities in published English-language Mizrahi life writing, I seek to contribute to a more accurate understanding of the diversity of Jewish experiences and the complexity of Jewish life and history in a Middle Eastern and North African context. I aim to develop a nuanced understanding of situations of displacement, dispersal, and resettlement. I demonstrate that memoir writing is a crucial genre for recording migratory experiences and transnational histories. This medium provides a vital and powerful tool that can aid in the recovery of psychological wellbeing and emotional resilience among women and men who have been displaced. An improved understanding of memory dynamics as well as the construction of identities and histories is all the more important in this present moment where dangerously simplistic divisions are often made at the expense of equity, diversity, and true human complexity

A Machine Learning Analysis of the Non- academic Employment Opportunities for Ph.D. Graduates in Australia

Can Australia's PhD graduates be better utilised in the non-academic workforce? There has been a historic structural decline in the ability of PhD graduates to find work within academia for the last couple of decades (Forsyth 2014). Around 60% of PhD graduates in Australia now find jobs outside the academy, and the number is growing year on year (McGagh et al. 2016). The PhD is a degree designed in the early 20th century to credential the academic workforce. How to make it fit contemporary needs, when many if not most graduates do not work in academia, is a question that must be addressed by higher education managers and policymakers. Progress has been slow, partly because of the lack of reliable data-driven evidence to inform this work. This paper puts forward a novel hybrid quantitative/qualitative approach to the problem of analysing PhD employability. We report on a project using machine learning (ML) and natural language processing to perform a 'big data' analysis on the text content of non-academic job advertisements. This paper discusses the use of ML in this context and its future utility for researchers. Using these methods, we performed an analysis of the extent of demand for PhD student skills and capabilities in the Australian employment market. We show how these new methods allow us to handle large, complex datasets, which are often left unexplored because of human labour costs. This analysis could be reproduced outside of the Australian context, given an equivalent dataset. We give an outline of our approach and discuss some of the advantages and limitations. This paper will be of interest for those involved in re-shaping PhD programs and anyone interested in exploring new machine learning methods to inform education policy work.The project team would like to thank the Australian Department of Industry and Seek.com.au for their generous support of this project

Tracking trends in industry demand for Australia’s advanced research workforce

This project successfully used Machine Learning to analyse job ads in order to better understand Australian industry demand for highly skilled researchers. Though further research and development work is required, The Machine developed in this project can be used to perform a longitudinal examination of Australian industry response to the innovation agenda

A Machine Learning Analysis of the Non-academic Employment Opportunities for Ph.D. Graduates in Australia

Can Australia’s Ph.D. graduates be better utilised in the non-academic workforce? There has been a historic structural decline in the ability of Ph.D. graduates to find work within academia for the last couple of decades (Forsyth in A history of the modern Australian University, New South Press, Sydney, 2014). Around 60% of Ph.D. graduates in Australia, now find jobs outside the academy, and the number is growing year on year (McGagh et al. in Securing Australia’s future: review of Australia’s research training system, https://acola.org.au/wp/PDF/SAF13/SAF13%20RTS%20report.pdf, 2016). The Ph.D. is a degree designed in the early twentieth century to credential the academic workforce. How to make it fit contemporary needs, when many, if not most, graduates do not work in academia, is a question that must be addressed by higher education managers and policymakers. Progress has been slow, partly because of the lack of reliable data-driven evidence to inform this work. This paper puts forward a novel hybrid quantitative/qualitative approach to the problem of analysing Ph.D. employability. We report on a project using machine learning (ML) and natural language processing to perform a ‘big data’ analysis on the text content of non-academic job advertisements. This paper discusses the use of ML in this context and its future utility for researchers. Using these methods, we performed an analysis of the extent of demand for Ph.D. student skills and capabilities in the Australian employment market. We show how these new methods allow us to handle large, complex datasets, which are often left unexplored because of human labour costs. This analysis could be reproduced outside of the Australian context, given an equivalent dataset. We give an outline of our approach and discuss some of the advantages and limitations. This paper will be of interest for those involved in reshaping Ph.D. programs and anyone interested in exploring new ML methods to inform education policy work

Collective effervescence: Designing MOOCs for emotion and community

This paper shares the experiences of a course team in designing and delivering a massive open online course (MOOC). It offers insight into how their approach can help build learning communities and enhance pedagogy for online learning through a return to best practice. It will discuss how a combined approach of using a core site in conjunction with social media platforms can temporarily overcome the functional limitations of xMOOCs, more deeply engage students, and improve moderation. Central to this, the concepts of collective effervescence and radical inclusion are shown to be effective principles of course design which facilitate ongoing support networks - an effective and sustained strategy for combating pluralistic ignorance within research education contexts

The use of machine learning to analyze job advertisements for doctoral employability

The nature and extent of the demand for research capable workers, is a topic of intense concern locally and internationally. With around 60% of graduates in Australia finding employment outside of academia on graduation, PhD programs are under increasing pressure to be relevant to the contemporary workplace beyond the walls of the academy. However, as yet, there is very little research on exactly what industry needs are as of the discussion with industry results in recommendations based on anecdote rather than data. This study aims to fill this gap by analysing a large data set of job ads to see what employers outside academia really want from graduates. This research builds on an exploratory study which analysed job adverts for roles specifying a PhD as a required or desired criteria in academic roles (Pitt and Mewburn, 2014). By focussing on what is actually stipulated as required for these roles at the time of advertising them, an alternative picture emerges of what employers really want in PhD-qualified employees. This next stage will use machine learning to investigate (and, should this project be successful, track) the demand for advanced research skills amongst Australian industry sectors. To do this we plan to systematically explore, annotate and catalogue job advertisements (drawn at first from Australia's largest online employment marketplace seek.com.au) advertising for highly paid knowledge workers. Data drawn from the SEEK database will be processed using language analysis and classification algorithms, allowing the development of a tool able to assess which Australian industry sectors are looking to hire researchers, and the skills they are looking for. This paper reports on the first phase of this project which involved building and testing a robust ontology for describing PhD graduate skills and its application to classify textual job ads retrieved from SEEK. Classifications of three content experts are compared. By making this data visible to research students, research managers, businesses and government we can find ways to better connect Australian businesses with Australia's highly skilled research workforce and provide new directions for those engaged in supporting PhD students in their career post PhD