Imagined Landscapes: Geovisualizing Australian Spatial Narratives

Imagined Landscapes teams geocritical analysis with digital visualization techniques to map and interrogate films, novels, and plays in which space and place figure prominently. Drawing upon A Cultural Atlas of Australia, a database-driven interactive digital map that can be used to identify patterns of representation in Australia’s cultural landscape, the book presents an integrated perspective on the translation of space across narrative forms and pioneers new ways of seeing and understanding landscape. It offers fresh insights on cultural topography and spatial history by examining the technical and conceptual challenges of georeferencing fictional and fictionalized places in narratives. Among the items discussed are Wake in Fright, a novel by Kenneth Cook, adapted iconically to the screen and recently onto the stage; the Australian North as a mythic space; spatial and temporal narrative shifts in retellings of the story of Alexander Pearce, a convict who gained notoriety for resorting to cannibalism after escaping from a remote Tasmanian penal colony; travel narratives and road movies set in Western Australia; and the challenges and spatial politics of mapping spaces for which there are no coordinates.

Romantic terrorism? Survivor narratives of psychological and emotional tactics of domestic violence

This paper draws on the theoretical arguments outlined in Hayes (2014) to frame critical analyses of two real life domestic violence narratives. The authors are both academic criminologists and victims/survivors of domestic violence, but within differing contexts – one a conventional heterosexual relationship, the other a female same-sex relationship. Their experiences are intertwined in an extensive collaborative auto-ethnographic analysis that spans seven years of working and socialising together, in which each provided a sounding board and support for the other. The analysis therefore documents two personal journeys. The academic and theoretical are intertwined with the personal and subjective to elicit an evocative and yet empirically validated study. The theoretical underpinnings of romantic love distortion, misogyny and sexism are used to frame these experiences of domestic violence and the differing sexualities of the authors provide a rich context for exploring the ways in which domestic violence victimisation experiences are impacted by gender, sexuality, and heteronormative discourses of love, sex and relationships.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Measuring and reporting on social performance: From numbers and narratives to a useful framework for social enterprises

This paper examines the need for a framework for social enterprises to measure and report on social performance. Reviewing social reporting practice, and concepts central to financial reporting, this paper presents a framework for social performance reporting in the context of social enterprises. A Statement of Social Performance is developed, through consideration of social reporting approaches, influences, and issues in third sector and private sector organisations. This Statement is applied in the context of an employment and training social enterprise, demonstrating its application in practice.