Surviving Supergentrification in Inner City Sydney: Adaptive Spaces and Makeshift Economies of Cultural Production

Artists and creative workers have long been recognized as playing an important role in gentrification, being often portrayed as forerunners of urban change and displacement in former industrial and working-class suburbs of ‘post-Fordist’ cities. However, as is well represented by recent research, the relationship between the arts, gentrification and displacement has been called into question. The purpose of this article, which draws on 30 case studies of creative spaces in Sydney's inner suburbs, is to chart some of the strategies of spatial adaptation and makeshift economies of solidarity that cultural workers adopt in order to keep living and working in areas of ‘supergentrification’. We document how cultural infrastructure is transformed by the gentrification process and argue that these alterations are critical to the survival of arts and culture in the city. Such makeshift economies contribute, in a practical way, to preserving the diversity that gentrification is sometimes deemed to destroy or displace. While the survival of creative spaces is a much less researched phenomenon than other forms of resistance or displacement, we suggest that it has important consequences for both research and policy decisions around gentrification, infrastructural development and urban cultural economies

Position estimating in peer-to-peer networks

We present two algorithms for indoor positioning estimation in peer-to-peer networks. The setup is a network of two types of devices: reference devices with a known location and blindfolded devices that can determine distances to reference devices and each other. From this information the blindfolded devices try to estimate their positions. A typical scenario is navigation inside a shopping mall where devices in the parking lot can make contact with GPS satellites, whereas devices inside the building make contact with each other, devices on the parking lot, and devices fixed to the building. The devices can measure their in-between distances, with some measurement error, and exchange positioning information. However, other devices might only know their position with some error. We present two algorithms for positioning estimation in such a peer-to-peer network. The first one is purely geometric and is based on Euclidean geometry and intersecting spheres. We rewrite the information to a linear system, which is typically overdetermined. We use least squares to ??nd the best estimate for a device its position. The second approach can be considered as a probabilistic version of the geometric approach. We estimate the probability density function that a device is located at a position given a probability density function for the positions of the other devices in the network, and a probability density function of the measured distances. First we study the case with a distance measurement to a single other user, then we focus on multiple other users. We give an approximation algorithm that is the probabilistic analogue of the intersecting spheres method. We show some simulated results where ambiguous data lead to well defined probability distributions for the position of a device. We conclude with some open questions

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Automated Essay Scoring in Australian Schools: Key Issues and Recommendations

This summary outlines critical issues associated with the use of Automated Essay Scoring (AES) technology in the Australian education system. The key insights presented in this paper emerged from a collaborative, multi-stakeholder workshop held in July 2022 that explored an automated essay-scoring trial and generated future possibilities aligned with participant interests and expertise. Drawing on the workshop and our expert understanding of the wider landscape, we propose recommendations that can be adopted by various stakeholders, schools, and educational systems. There are compelling reasons for Australian schools and education departments to investigate the use of AES. AES could potentially alleviate aspects of teachers’ workload at a time when teacher attrition is historically high and teacher recruitment historically low. At the same time, AES also has the potential to de-professionalise and deskill of teachers. Educationalists are acutely aware that quality feedback can help students improve their learning across multiple subjects and domains, however parents and many are reluctant to hand that responsibility over to AES. In 2018, concerns among teachers, teachers’ unions, principles, and parents became apparent when the federal Department of Education, Skills and Employment attempted to implement a form of AES in The National Assessment Program – Literacy and Numeracy (NAPLAN). These concerns primarily registered around three issues: − de-professionalisation of teachers, − inequitable infrastructure in Australian schools, and − lack of transparency from examination authorities as to how marking decisions are made. The use of AES in NAPLAN ultimately proved to be politically unpopular, leading to its suspension. However, the growing implementation of AES in schools across the globe means that the use of this technology is likely to re-emerge as a controversial issue in Australia. Without political leadership in this area, it is ultimately up to educational institutions and agencies, policymakers, and school communities to assess the benefits and pitfalls of AES and navigate the way forward. Our recommendations will assist the emergence of good governance in this area. To begin, it is crucially important to identify whether AES will be used in high-stakes or low-stakes tests. High-stakes tests are defined as those with consequential outcomes for students or educators, such as the determination of progression of students or rankings of school institutions. If AES is to be used in Australian schools, the following issues must be considered: − the capacity of stakeholders, including principals, teachers, and parents, to understand how AES systems work − the infrastructure required to support the use of AES − the potential impacts of AES on assessment and workload practices which requires adequate professional development resources − competing interests and values between schools, departments, and institutions associated with using AES − how the use of AES relates to and integrates with broader policy frameworks. The investigation of these issues requires information sharing, dialogue, and negotiation among diverse stakeholders, including teachers, parents, students, leaders, and policymakers. In addition to this engagement, schools and other educational institutions must also discuss the implementation of AES tools with AES system developers and commercial vendors, so as to better understand the functions and limitations of the AES tool, as well as its implications for professional and assessment practices. Only then can decision-makers evaluate whether a specific AES system is worth the investment of funds and resources, including teacher workload, in both the medium and longer term. Although it appears as yet another drag on teacher time, the participatory and collaborative development of AES guidance, policy, and regulation is crucial. It ensures that pluralistic views and shared values are reflected in any innovations or reforms across the education sector. To ensure a collaborative foundation, the introduction of AES must be informed by stakeholder expertise across multiple locations and decision-making levels, including classrooms, schools, organisations, and state, territory, and national jurisdictions. For Australia, we recommend multi-scalar policy development informed by educators, policymakers, and representatives from educational technology companies engaging in cooperative learning and action

Position estimating in peer-to-peer networks

We present two algorithms for indoor positioning estimation in peer-to-peer networks. The setup is a network of two types of devices: reference devices with a known location and blindfolded devices that can determine distances to reference devices and each other. From this information the blindfolded devices try to estimate their positions. A typical scenario is navigation inside a shopping mall where devices in the parking lot can make contact with GPS satellites, whereas devices inside the building make contact with each other, devices on the parking lot, and devices fixed to the building. The devices can measure their in-between distances, with some measurement error, and exchange positioning information. However, other devices might only know their position with some error. We present two algorithms for positioning estimation in such a peer-to-peer network. The first one is purely geometric and is based on Euclidean geometry and intersecting spheres. We rewrite the information to a linear system, which is typically overdetermined. We use least squares to ??nd the best estimate for a device its position. The second approach can be considered as a probabilistic version of the geometric approach. We estimate the probability density function that a device is located at a position given a probability density function for the positions of the other devices in the network, and a probability density function of the measured distances. First we study the case with a distance measurement to a single other user, then we focus on multiple other users. We give an approximation algorithm that is the probabilistic analogue of the intersecting spheres method. We show some simulated results where ambiguous data lead to well defined probability distributions for the position of a device. We conclude with some open questions