With global population ageing and shifting morbidity and disability patterns, the demand for long-term care is increasing. The chapter highlights the impact of demographic changes, particularly the rise in the older population and the growing need for dementia care, on long-term care demand. It advocates for a paradigm shift from institutionalized nursing homes to home-based care and stresses the need for policy support to enhance informal caregiving and develop a robust long-term care workforce. Additionally, it underscores the significance of recognizing long-term care as a social and human right and establishing a regulatory framework to ensure high-quality care.

The aim of this chapter is to illustrate how a robust long-term care system can positively influence the overall wellbeing of society, extending beyond the individual receiving care. Since the primary recipients of long-term care are often older or disabled individuals, it is sometimes viewed as a costly burden on society rather than an investment in the public interest or the common good. This chapter seeks to challenge such perceptions by emphasizing the positive and proactive social impact of a strong long-term care system on society as a whole. By highlighting these arguments, the chapter aims to provide further justification for countries to invest in their long-term care systems.

This chapter explores methods of financing long-term care. These include public financing, which involves government-managed programs funded through taxation and social insurance schemes, and private financing, which includes out-of-pocket payments and family contributions. The chapter also considers how resources are pooled and allocated, as well as policy decisions regarding public care coverage and financial protection. Short case studies illustrate the practical functioning of different financing models. Finally, the chapter considers the evolution of long-term care expenditure in the context of an ageing population.

This chapter explores the available evidence on how long-term care influences health systems. While the focus is primarily on high-income countries, the issues discussed are relevant to low- and middle-income countries facing rising demands for long-term care as populations age. Overall, the literature suggests a strong long-term care system has many positive consequences for the health sector and for the health and well-being of older people.

Behind the black boxes of algorithms promoting or adding friction to posts, technical design decisions made to affect behavior, and institutions stood up to make decisions about content online, it can be easy to lose track of the heteromation involved, the humans spreading disinformation and, on the other side, moderating or choosing not to moderate it. This can be aptly shown in the case of the spread of misinformation on WhatsApp during Brazil’s 2018 general elections. Since WhatsApp runs on a peer-to-peer architecture, there was no algorithm curating content according to the characteristics or demographics of the users, which is how filter bubbles work on Facebook. Instead, a human infrastructure was assembled to create a pro-Bolsonaro environment on WhatsApp and spread misinformation to bolster his candidacy. In this paper, we articulate the labor executed by the human infrastructure of misinformation as hetoromation.

Misinformation pervades our everyday lives, ranging from dangerous to harmless, malicious to well-intentioned. Intentional deception is a wide-reaching form of misinformation, that is sometimes harmful and sometimes socially positive or acceptable. Deception includes various behaviors that obscure the truth, mislead, or promote falsehoods. Misrepresentation, in certain instances, may be misleading or false but is normatively expected as standard practice. Thus, we ask: What aspects of deception produce different perceptions? How can community governance of misinformation adjust based on social perceptions and norms? This chapter addresses these questions, exploring governance of everyday misinformation in three Instagram domains: dating, food, and retail.

Storytelling is everyday information behavior that, when it goes wrong, can propagate misinformation. From accurate data to misinformed stories, what goes wrong with the process? This chapter focuses on the dynamics of storytelling in misinformation as a problematic aspect of the COVID-19 pandemic in three widely circulated problematic stories. Storytelling offers a framework for researching collective experiences of information as a process that is inherently based in communities, with knowledge commons that are instantiated by the telling and retelling of stories, temporarily or permanently. To understand how difficult information is to govern in story form and through storytelling dynamics, this chapter uses storytelling theory to explore three recent cases of COVID-19 misinformation related to medicine misuse, exploiting vaccine hesitancy, and aftermath of medical racism. Understanding what goes wrong with these stories may be key to public health communications that engage effectively with communitiesÕ everyday misinformation challenges.Ê

Information hazing is the use of information to directly and indirectly harass and/or exclude newcomers. This is common in spaces with strong social cohesion where the dominant group is wary of accepting individuals who do vary from the group. The tech industry and its pipeline, computer science education, are two places where the lack of diverse and varied voices has led to numerous social harms. We have collected 30 syllabi from CS1 courses across the US to explore how the courses governing documents, and syllabi, curate the computer science education knowledge commons. Our evaluation highlights areas of policy, research, and student perspectives that are out of alignment both with practice in academia and industry standards. Requirements stemming from the expectation of independent assessment within the academic environment versus the common practice of open information and collaboration appear to clash within the academic integrity policies of many computer science courses. These competing priorities create opportunities for undue harm that create a fertile ground for the spread of misinformation, disinformation, and malinformation. These are usually the unanticipated consequences of policies written in good faith, but still exhibit the toxic, stressful, and isolating impacts of hazing.

Misinformation has shown itself in recent years to be an incredibly complicated and thorny societal problem. While most academic work on the subject looks at the largest and scariest examples of misinformation, it largely ignores the fundamental reality that to misinform is to be an imperfect person also known as a human. In this Introduction to the edited volume, Governing Misinformation in Everyday Knowledge Commons, the editors briefly explain the three scholarly traditions (The Everyday, Misinformation, and Governing Knowledge Commons) that are the nexus of each chapter in this tome. We also present several illustrative examples to highlight the thesis of this work, that misinformation is incredibly commonÊand only through addressing the context and risk around it can we create nuanced and culturally specific solutions.