The introduction begins with the crisis of republicanism in France today. It asks what republicanism is for the French and why exclusion lies at the center of its crisis. It then presents the characteristics of French republicanism (in particular, its universalism and its emancipatory dimension), its place in the broader republican tradition, and in the neo-republican revival. Finally, it introduces the theoretical paradoxes that led republicans to justify exclusionary practices despite their endorsement of emancipation.

The chapter documents how U.S. economic growth, labor productivity growth, and labor’s share of output have evolved over time. Issues concerning the estimation of the economy’s productive potential are discussed, including problems associated with factor aggregation, the theoretical and empirical use of production functions, and growth accounting. The role that estimates of potential output should play in the policy process is also considered.

Productive scholars make writing a daily habit. Some aim for five pages a day, others an hour or two or a single paragraph. Psychologically, they know they’re making progress. Productive scholars are chronic organizers, using frameworks to organize their thoughts before writing and helping them spot relationships and patterns as they write. As they write, they suspend judgment about writing form and mechanics and let the words flow. Later they edit and revise to perfection. They rarely start at the beginning, instead beginning with tables and graphics, then methods and results, and finally the introduction and discussion. Scholars inject their personality. That might include an opening metaphor, a rhythmic sentence cadence, flowerful language, or humorous titles. They tell clear, coherent, and memorable stories, easily understood and appreciated by a friend. They use time-honored writing conventions like topic sentences, signals, and transitions to lead the reader comfortably through the story. They don’t confuse readers with jargon and acronyms. Scholars don’t let their writing sit too long and get cold, yet they give it time to set when finished, and then return to it with fresh eyes. They welcome feedback for improvement, which might come from writing group members.

This chapter goes back to the arguments about the importance of context for design-oriented research on women’s work. It addresses questions such as: What are the most relevant aspects of context, how much do designers need to know about them, and what are the methods that can help them understand and deal with contextual elements in their work? The chapter revisits concepts that help understand contexts and their epistemological roots and discusses approaches to dealing with context in practical terms: learning about the history of a place and its culture; understanding politics, policymaking, and the institutional/organizational context; getting a hold on working conditions and skills; making space for intersectionality. The chapter includes a retrospective analysis of two of the authors’ own design/research projects, looking into how they dealt with context. It formulates a set of questions intended to help designers develop strategies that will maintain a sensitivity towards gender issues.

Extension’s charge has long been to extend research, information, and innovative strategies and programs from Land Grant University and research centers. Extension is working in an era of digital communication where social media is evidencing extraordinary growth. Most adults in the United States now report accessing information from social media platforms. As such the use of social media and technology in Extension is rapidly becoming a necessity. As Extension has served as a trusted source of information for over a century, it is currently facing a time of transition to explore how to effectively use technology to reach audiences. This chapter explores current trends in digital communication and highlights the ways that Extension can create and deliver information using social media. We propose intentionality in strategies and factors to consider when engaging via social media. Finally, we pose questions regarding Extension’s continued relevance and possibly changing role in considering the rapidly shifting information landscape.

Author Kiewra has submitted many manuscripts, served on several editorial boards, and was a journal editor. He finds the review process flawed. Ask three academics to review a manuscript and expect an arms race of criticisms and recommendations. Also expect editors to behave like managers instead of scholars. Many line up reviewers, parrot their comments, and make no judgment of their own. Regardless, authors must know how to handle submissions that were rejected or given a second chance. If rejected, join the club. Top-tier journals reject about 90 percent of submissions, so don’t get discouraged. One productive scholar said, “You have to learn how to take criticism and rejection because we’re pretty critical of each other, and reviewers can sometimes be brutal.” Consider resubmitting your work elsewhere. Another scholar said, “You can always find a home for a paper in a second-tier journal.” Do a happy dance if you receive a revise and resubmit decision. The reviews provide a roadmap for acceptance. Follow that revision map. When you resubmit, include a response letter that specifies how and where you addressed each reviewer point. Also, accept the blame and be polite, respectful, thankful, and positive.

This chapter follows the debate of data work, without which AI-based technologies would not exist. It introduces concepts that capture bias in datasets and algorithms from a feminist data ‘ethics of care’ approach and discusses approaches to avoid or countervail bias. The chapter then turns to work and the question how to make AI-based technologies work in practice. Examples, most of them from IT development and health care, help understand the centrality of care, trust, and human–algorithm collaborations, what is often called ‘the human in the loop’, as key elements determining the usefulness of AI-based systems and tools to work. Trust and care are central to data and algorithmic stewardship, which will need to be mobilized in relation to AI and machine learning if we are going to achieve gender justice in future work processes.

This chapter examines facial recognition technology (FRT) and its potential for bias and discrimination against racial minorities in the criminal justice system. The chapter argues that by defining the technology as an automated process, there is an implied objectivity, suggesting that such technologies are free from errors and prejudices. However, facial recognition is dependent on data used to train an algorithm, and operators make judgements about the wider social system and structures it is deployed within. The algorithms that underpin FRT will continue to advance the status quo with respect to power relations in the criminal justice system, unless both data-based and societal-based issues of inequality and discrimination are remedied. FRT is imbued with biases that can negatively impact outcomes for minority groups. The chapter argues that there is a need to focus on systemic discrimination and inequality (rather than calling for a ban of the technology). While the data-based issues are more straightforward to address, this alone will not be sufficient: addressing broader and more complex social factors must be a key focus in working towards a more equal society.