Close Encounters of the Continuum: Speaking (to), Performing and Experiencing Gender In Janet Cardiff’s Whispering Room

This 'project' investigates Janet Cardiff's Whispering Room. It examines how Cardiff deconstructs the privileging of the visual over all other corporeal senses in her work, the Whispering Room. Using sound as a fulcrum, Cardiff explores the links between subjects, collective narratives, memories, experiences and performances. Janet Cardiff destabilizes time and space and fractures the continuum through the use of sound. My 'project' celebrates sound as a transgressive medium — sound not as a gendered medium but as a vehicle in which to speak (to) gender. It explores how sound can destabilize notions of perception and reception and question art and museal practices. In the process this 'project' reveals the complexity of interpreting and representing art as an object. My aim is to reflect the very intertextual and expressionist collage that Cardiff has created in Whispering Room in my own text. Cardiff solicits the viewer's intimacy and participation. Whispering Room is a physical yet metonymic space in which Cardiff creates a place for performatvity, experience, memory, desire and speech, thus she opens up a space for the utterance and performance of the viewer. Viewers construct and create meaning/s for themselves within this mnemonic space by digging up their own memories, desires and reveries. The strength of Cardiff's work is that it relies on a viewer to perform, a body to trigger the pseudo-spectacle and a voice to interrupt the whispers. One might ask of Whispering Room where the illusionistic space begins and where the physical space ends. This 'project' investigates how in Whispering Room there is no one experience but many experiences.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

One device, two doses

Ms Breathless is a 59-year-old lady living with chronic obstructive pulmonary disease (COPD) for several years now, and quit smoking over 20 years ago. She sometimes experiences symptoms of breathlessness, and an unrelenting productive cough, particularly when she comes in contact with dusty places, second-hand smoke, or when her allergies and hay fever play up. Apart from these triggers, her symptoms are well maintained with her inhalers glycopyrronium bromide (Seebri) and indacaterol (Onbrez), and she has been using them for about nine months. Ms Breathless is otherwise healthy, and not taking any other medicines.