Supervising practice: Perspectives on the supervision of creative practice higher degrees by research

PhD supervision is a particularly complex form of pedagogical practice, and nowhere is its complexity more apparent than in new and emergent fields, such as creative practice Higher Degrees by Research (HDRs) where supervisors face the challenges of a unique, uncharted area of research training. While there is an increasing body of literature on postgraduate supervision, and another emerging body of research into what creative practice/practice-led/practice-based research is, so far little attention has been paid to matters associated with research education leadership and pedagogical aspects of supervision in creative practice disciplines.For this reason, this special issue brings together a range of perspectives on the supervision of creative practice PhDs in visual and performing arts, media production, creative writing, and design.

Critically deconstructing race through a multimodal analysis of iPad Tellagami videos

Drawing on multimodal texts produced by an Indigenous school community in Australia, I apply critical race theory and multimodal analysis (Jewitt, 2011) to decolonise digital heritage practices for Indigenous students. This study focuses on the particular ways in which students’ counter-narratives about race were embedded in multimodal and digital design in the development of a digital cultural heritage (Giaccardi, 2012). Data analysis involved applying multimodal analysis to the students’ Gamis, following social semiotic categories and principles theorised by Kress and Bezemer (2008), and Jewitt (2006, 2011). This includes attending to the following semiotic elements: visual design, movement and gesture, gaze, and recorded speech, and their interrelationships. The analysis also draws on critical race theory to interpret the students’ representations of race. In particular, the multimodal texts were analysed as a site for students’ views of Indigenous oppression in relation to the colonial powers and ownership of the land in Australian history (Ladson-Billings, 2009). Pedagogies that explore counter-narratives of cultural heritage in the official curriculum can encourage students to reframe their own racial identity, while challenging dominant white, historical narratives of colonial conquest, race, and power (Gutierrez, 2008). The children’s multimodal “Gami” videos, created with the iPad application, Tellagami, enabled the students to imagine hybrid, digital social identities and perspectives of Australian history that were tied to their Indigenous cultural heritage (Kamberelis, 2001).

Dysbindin (DTNBP1) variants are associated with hallucinations in schizophrenia

BACKGROUND: Dystrobrevin binding protein 1 (DTNBP1) is a schizophrenia susceptibility gene involved with neurotransmission regulation (especially dopamine and glutamate) and neurodevelopment. The gene is known to be associated with cognitive deficit phenotypes within schizophrenia. In our previous studies, DTNBP1 was found associated not only with schizophrenia but with other psychiatric disorders including psychotic depression, post-traumatic stress disorder, nicotine dependence and opiate dependence. These findings suggest that DNTBP1 may be involved in pathways that lead to multiple psychiatric phenotypes. In this study, we explored the association between DTNBP1 SNPs (single nucleotide polymorphisms) and multiple psychiatric phenotypes included in the Diagnostic Interview of Psychosis (DIP). METHODS: Five DTNBP1 SNPs, rs17470454, rs1997679, rs4236167, rs9370822 and rs9370823, were genotyped in 235 schizophrenia subjects screened for various phenotypes in the domains of depression, mania, hallucinations, delusions, subjective thought disorder, behaviour and affect, and speech disorder. SNP-phenotype association was determined with ANOVA under general, dominant/recessive and over-dominance models. RESULTS: Post hoc tests determined that SNP rs1997679 was associated with visual hallucination; SNP rs4236167 was associated with general auditory hallucination as well as specific features including non-verbal, abusive and third-person form auditory hallucinations; and SNP rs9370822 was associated with visual and olfactory hallucinations. SNPs that survived correction for multiple testing were rs4236167 for third-person and abusive form auditory hallucinations; and rs9370822 for olfactory hallucinations. CONCLUSION: These data suggest that DTNBP1 is likely to play a role in development of auditory related, visual and olfactory hallucinations which is consistent with evidence of DTNBP1 activity in the auditory processing regions, in visual processing and in the regulation of glutamate and dopamine activity

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.