(p.432) In the United Kingdom, the pronounced increase in self-employment, around the turn of the century, has more recently been accompanied by the rise of so-called ‘gig work’. In particular, the spread of Uber – often hailed as the flagship of the gig economy – has given rise to a spirited debate. On the one hand, it has been argued that Uber extends the opportunity to become a ‘micro-entrepreneur’ to groups often marginalized in the traditional labour market. By giving individuals full autonomy over working time, it allows drivers to achieve work–life balance and provides opportunities to earn additional income when...

(p.467) The preference for flexible work expressed by Uber drivers, which we find to be correlated with their SWB, suggests that evaluations of the gig economy preferably should go beyond monetary metrics. Notably, an important role of non-monetary factors also extends to more traditional work arrangements. Indeed, the latest British Social Attitudes survey shows that less than half feel that work is only about monetary compensation, and the importance people attach to income has been falling in recent years. Thus, happiness economics seemingly has an important role to play in the context of evaluating workers’ welfare in the future of work.

"Social reproduction, when valued by the market, is gendered, often racialized, and poorly remunerated. Where states no longer provided support and where purchasing services on the market was not feasible, the burden of providing additional care and work fell onto families, especially women. In Ontario under the Conservatives (1995-2003), this familializing and individualizing thrust was underlined by a rhetoric about family values and a nostalgic idealization of motherhood and community. As material supports for communities and families were cut, this family ideology blamed families--and mothers in particular--for failing to take responsibility for their members." p. 6

(p.502) As dependence on computers has increased, automated methods for the identification and processing of these predefined features — collectively known as computer-aided detection (CADe) — have long been proposed and occasionally utilized in the clinic. Radiologist-defined criteria are distilled into a pattern-recognition problem where computer vision algorithms highlight conspicuous objects within the image. However, these algorithms are often taskspecific and do not generalize across diseases and imaging modalities. Additionally, the accuracy of traditional predefined featurebased CADe systems remains questionable, with ongoing efforts to reduce false positives. It is often the case that outputs have to be assessed by radiologists to...