Towards a middle-range theory of mental health and well-being effects of employment transitions: Findings from a qualitative study on unemployment during the 2009-2010 economic recession.

This article builds upon previous theoretical work on job loss as a status passage to help explain how people's experiences of involuntary unemployment affected their mental well-being during the 2009-2010 economic recession. It proposes a middle-range theory that interprets employment transitions as status passages and suggests that their health and well-being effects depend on the personal and social meanings that people give to them, which are called properties of the transitions. The analyses, which used a thematic approach, are based on the findings of a qualitative study undertaken in Bradford (North England) consisting of 73 people interviewed in 16 focus groups. The study found that the participants experienced their job losses as divestment passages characterised by three main properties: experiences of reduced agency, disruption of role-based identities, for example, personal identity crises, and experiences of 'spoiled identities', for example, experiences of stigma. The proposed middle-range theory allows us to federate these findings together in a coherent framework which makes a contribution to illuminating not just the intra-personal consequences of unemployment, that is, its impact on subjective well-being and common mental health problems, but also its inter-personal consequences, that is, the hidden and often overlooked social processes that affect unemployed people's social well-being. This article discusses how the study findings and the proposed middle-range theory can help to address the theoretical weaknesses and often contradictory empirical findings from studies that use alternative frameworks, for example, deprivation models and 'incentive theory' of unemployment

Titania-doped tantala/silica coatings for gravitational-wave detection

Reducing thermal noise from optical coatings is crucial to reaching the required sensitivity in next generation interferometric gravitational-wave detectors. Here we show that adding TiO2 to Ta2O5 in Ta2O5/SiO2 coatings reduces the internal friction and in addition present data confirming it reduces thermal noise. We also show that TiO2-doped Ta2O5/SiO2 coatings are close to satisfying the optical absorption requirements of second generation gravitational-wave detectors

Heritage Quay: What Will You Discover? Transforming the Archives of the University of Huddersfield, Yorkshire, UK

The Heritage Quay project is changing how archive services at the University of Huddersfield are delivered. This article examines how the Staff/Space/Collections dependency model and Customer Service Excellence framework have been used, and what lessons can be drawn for other archives

Sustainability limits needed for CO2 removal

International audienceMany governments and industries are relying on future large-scale, land-based carbon dioxide (CO2) removal (CDR) to avoid making necessary steep greenhouse gas (GHG) emission cuts today (1, 2). Not only does this risk locking us into a high overshoot above 1.5°C (3), but it will also increase biodiversity loss, imperiling the Kunming-Montreal Global Biodiversity Framework (KMGBF) goals (4). Such CDR deployments also pose major economic, technological, and social feasibility challenges; threaten food security and human rights; and risk overstepping multiple planetary boundaries, with potentially irreversible consequences (1, 5, 6). We propose three ways to build on the Intergovernmental Panel on Climate Change (IPCC) analyses of CDR mitigation potential by assessing sustainability risks associated with land-use change and biodiversity loss: estimate the sustainable CDR budget based on socioecological thresholds; identify viable mitigation pathways that do not overstep these thresholds; and reframe governance around allocating limited CDR supply to the most legitimate uses

Transforming Big Data into AI‐ready data for nutrition and obesity research

OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions. METHODS: We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed. RESULTS: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented. CONCLUSIONS: Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions

Ethical choices behind quantifications of fair contributions under the Paris Agreement

The Parties to the UNFCCC and Paris Agreement agreed to act on the basis of equity to protect the climate system. Equitable effort sharing is an irreducibly normative matter, yet some influential studies have sought to create quantitative indicators of equitable effort that claim to be value-neutral (despite evident biases). Many of these studies fail to clarify the ethical principles underlying their indicators, some mislabel approaches that favour wealthy nations as ‘equity approaches’ and some combine contradictory indicators into composites we call derivative benchmarks. This Perspective reviews influential climate effort-sharing assessments and presents guidelines for developing and adjudicating policy-relevant (but not ethically neutral) equity research

CAVRN Syllabus, Vol. 1

In this inaugural volume, we introduce CAVRN and set out an agenda for a Critical Augmented and Virtual Reality research Network. Through what we refer to as ‘critical AR and VR studies’, we argue there is urgent need for research that takes stock of rapid developments in the AR and VR space – accounting for the ethical, social, political, and economic implications of these technologies. This volume of CAVRN presents 16 contributions offering critical perspectives on AR and VR, encompassing diverse domains, united in their call for a deeper exploration of the complexities of virtual interaction, advocating for an approach to the critique of VR that accounts for both its material-technical affordances and its socio-cultural dimensions. The contributions in this volume cover four main areas – 1) the policy, regulatory, and legal implications of AR and VR, 2) media theoretical approaches to studying VR, 3) responses to the emerging ‘metaverse’, and 4) VR experiences and storytelling