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While the main emotional labor strategies are well-documented, the manner in which professionals navigate emotional rules within the workplace and effectively perform emotional labor is less understood. With this contribution, I aim to unveil “the good, the bad and the ugly” of emotional labor as a dynamic theatrical performance.

Focusing on three geriatric long-term care units within a French public hospital, this qualitative study relies on two sets of data (observation and interviews). Deeply rooted within the field of study, the chosen methodological approach substantializes the subtle hues of the emotional experience at work and targets resonance rather than generalization.

Using the theatrical metaphor, this research underlines the role of space in the practice of emotional labor in a unique way. It identifies the main emotionalized zones or emotional regions (front, back, transitional, mixed) and details their characteristics, before unearthing the nonlinearity and polyphonic quality of emotional labor performance and the versatility needed to that effect. Indeed, this research shows how health-care professionals juggle with the specificities of each region, as well as how space generates both constraints and resources. By combining static and dynamic prisms, diverse instantiations of hybridity and spatial in-betweens, anchored in liminality and trajectories, are revealed.

This research adds to the current body of literature on the concept of emotional labor by shedding light on its highly dynamic and interactional nature, revealing different levels of porosity between emotional regions and how the characteristics of each type of area can taint others and increase/decrease the occupational health costs of emotional labor. The study also raises questions about the interplay of emotional labor performance with the level of humanization/dehumanization of elderly people. Given the global demographics about an aging population, this gives food for thought at a social level.

Apart from the challenges related to vehicle technology, the wide-scale deployment of autonomous vehicles (AVs) in cities is linked to unprecedented opportunities and unforeseen impacts. These refer to mobility conditions, infrastructure, land use, wider socio-economic factors, energy use and environmental and climate effects. AVs may affect all these in positive or negative ways, promoting or obstructing the promotion of specific aspects of sustainable urban development. An integrated planning framework is needed to maximise the positive impacts and mitigate the negative ones. The main obstacle in the process of developing such a framework is the absence of empirical data and experience from the implementation of this emerging technology. This chapter outlines the possible impacts of AVs and discusses their uncertainty and trade-offs in relation to sustainable urban development. The categorisation of impacts derives from the priorities of the UN Sustainable Development Goal (SDG) 11: Make cities and human settlements inclusive, safe, resilient, and sustainable. The chapter also highlights the lack of data for the development of an evidence-based planning approach and suggests relevant recommendations to planners. In contrast to the current lack of data, the future abundance of Big Data collected by autonomous road transport systems is discussed in the context of future urban planning purposes. Based on the above, the chapter concludes by stressing the importance of an integrated urban transport planning approach that ensures a positive contribution of AVs to sustainable urban development. Hence, it offers valuable recommendations for policymakers in a range of fields.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.