Privacy has traditionally been conceptualized in an individualistic framing, often as a private good that is traded off against other goods. This chapter views the process of privacy enforcement through the lens of governance and situated design of sociotechnical systems. It considers the challenges in formulating and designing privacy as commons (as per the Governing Knowledge Commons framework) when privacy ultimately gets enacted (or not) in complex sociotechnical systems. It identifies six distinct research directions pertinent to the governance and formulation of privacy norms, spanning an examination of how tools of design could be used to develop design strategies and approaches to formulate, design, and sustain a privacy commons, and how specific technical formulations and approaches to privacy can serve the governance of such a privacy commons.

Here we provide an overview of Part I, introducing the main themes we will address as they relate to the science of careers. We ask what mechanisms drive productivity and impact, how creativity is distributed over the course of a career, and whether a scientist’s highest impact work can tell us anything about the other work they produce.

Here we explore the mechanisms and drivers behind the impact disparity discussed in the previous chapter, focusing on what factors create high-impact papers and what conditions contribute to the lognormal distribution citations follow. We show how a rich-get-richer phenomenon similar to preferential attachement, growth, and fitness all contribute to the impact of a paper. We describe a fitness model that can effectively represent these dynamics, providing insight into how impact is created in science.

We ask if it’s possible to accelerate the advancement of science by applying the science of science to the frontiers of knowledge. Using a robot scientist as an example, we show how it is now possible to close the loop by building machines that can create scientific knowledge. We discuss the implications of this on the future of the discipline. Another way to more efficiently advance science is to generate more fruitful hypotheses. We discuss the Swanson hypothesis, which provides a window into how to hone in on valuable discoveries, allowing for the forecasting of frutiful areas of research. We then explore how the frontiers of science can be traced, allowing scientists to more thoughtfully choose topics that will accelerate collective discovery. Finally, we address some challenges posed by this issue, including the “file drawer problem,” which could be mitigated by a more systemic approach to sharing negative results with colleagues in the discipline. We suggest several ways to incentivize and reward impactful science so that we can efficiently reap its benefits.

We begin by showing that age-specific patterns affect the allocation of funding in science. We then ask if there are age specific patterns that dictate when a scientist does her best work, and show that there are universal trends in the age distribution of great innovation. We offer possible explanations as to why these patterns occur. One explanation, which helps explain why scientists typically reach peak performance in middle age, is the “burden of knowledge” theory. Yet this explanation doesn’t account for the discipline-specific trends in age at peak performance that complicate the picture, which may be accounted for by the type of work produced. Research shows that there are two kinds of innovators–conceptual and experimental–and that each has a different peak. Experimental innovators, who accumulate knowledge through experience, tend to peak later. Conceptual innovators, who apply abstract principles, tend to peak earlier. We end by discussing Planck’s principle, which posits that young and old scientists have differing affinities for accepting new ideas.

Here, we focus on two factors that contribute to a paper’s fitness: novelty and publicity. By measuring the novelty of the ideas shared in a paper, we can explore the link between the originality of the research and its impact. Since new ideas are typically snythesized from existing knowledge, we can assess the novelty of an idea by looking at the number domains from which researchers sourced their ideas and how expected or unexpected the combination of domains are. Evidence shows that rare combinations in scientific publications or inventions are associated with high impact. Yet novel ideas are riskier than conventional ones, frequently resulting in failure. Research indicates that scientists tend to be biased against novelty, making unconventional work more difficult to get off the ground. In order to mitigate risk while maximizing novelty, scientists must balance novelty with conventionality. We then look at the role that publicity plays in amplifying a paper’s impact. We find that publicity, whether good or bad, always boosts a paper’s citation counts, indicating that, even in science, it’s better to receive negative attention than no attention at all.

We begin with an anecdote about the largest team in scientific history, and then discuss the shift toward larger teams more generally. We show that the team size distribution has changed its fundamental shape since the 1950s, shifting from a Poussion distribution to a power law distribution as teams have grown larger. These two mathematical shapes represent different modes in which teams form. An exponential distribution leads to the creation of small “core” teams. A power-law distribution results in “extended” teams, accumulating new members in proportion to the productivity of their existing members. These two modes allow us to create an accurate model of team formation, providing us with insight about how team size affects its survival, longevity, and creation of knowledge. We can then assess some of the benefits and drawbacks of large teams, and explore the different kinds of science large and small teams produce. We show how to quantify the disruption of an idea by creating a disruption index, and explain how levels of disruption reflect team size. We end by discussing the implications of the shift to larger teams in science, making a case for preserving smaller teams.

The Internet of Everything takes the notion of IoT a step further by including not only the physical infrastructure of smart devices, but also its impacts on people, business, and society. Our world is getting more connected, if not smarter, but to date governance regimes have struggled to keep pace with this dynamic rate of innovation. Yet it is an open question whether security and privacy protections can or will scale within this dynamic and complex global digital ecosystem, and whether law and policy can keep up with these developments? The natural question, then, is whether our approach to governing the Internet of Everything is, well, smart? This chapter explores what lessons the Institutional Analysis and Development (IAD) and Governing Knowledge Commons (GKC) Frameworks hold for promoting security, and privacy, in an Internet of Everything, with special treatment regarding the promise and peril of blockchain technology to build trust in such a massively distributed network. Particular attention is paid to governance gaps in this evolving ecosystem, and what state, federal, and international policies are needed to better address security and privacy failings.

Using a story of credit allocation gone wrong, we introduce some of the challenges that come with assigning credit to collaborative work in science, espeically given the historicial emphaisis on individual acheivement in our field. We explore traditional methods for indicating ownership of scientific work, particularly the ordering of authors on a paper. While this method for understanding who should get the lion’s share of the credit for a discovery is usually effective, it is complicated by discipline-specific variations in the order of authorship. We also look at how alphabetical ordering of authorship in some fields further complicates the picture, how “guest authors” and “ghost authors” reflect flaws in the credit allocation system, and how bias affects the process adversely. We end with a discussion of the alarming colloboration penalty women economists experience, which illustrates the mishaps that can and do occur as a result of the existing system.

Citations reflect the cumulative nature of science, where new research builds on previous discoveries. While citations have been previously used to condense and signal knowledge, they have been employed more recently to gauge the scientific impact of a particular paper or discovery. Groundbreaking work, the thinking goes, should be highly cited by other scientists. In Part III, we explore how scientific impact is quantified, focusing less on the producers of science and more on the work that is produced.

To provide a framework for understanding the importance of team assembly, we open the chapter with a story about teams of chickens. This leads into a discussion about the “too much talent” effect in a range of arenas. We then discuss the role that diversity plays in scientific teamwork. Studies show that diversity among team members – whether ethnic, international, or institutional – promotes the team’s effectiveness, with ethnic diversity offering the largest boost in impact of the resulting paper. We also define collective intelligence and explore that factors that lead to highly intelligent teams. Finally, by mapping the larger networks team members are involved in, we’ve identified four types of links that influence group effectiveness. By varying the proportion of these types of links within a network, we can see how certain coauthorship patterns will impact a team’s success. Taken together, these results show a strong correlation between a team’s composition and the quality of the work it produces. We end by discussing super-ties, or extremely close working relationships that become scientific partnerships, which yield surprising citation and productivity premiums.

Understanding the rules and norms that shape the practices of institutional researchers and other data practitioners in regards to student data privacy within higher education could be researched using descriptive methods, which attempt to illustrate what is actually being done in this space. But, we argue that it is also important for practitioners to become reflexive about their practice while they are in the midst of using sensitive data in order to make responsive practical and ethical modulations. To achieve this, we conducted a STIR, or socio-technical integration research. We see in the data, the STIR of a single institutional researcher, some evidence of changes in information flow, reactions to it, and ways of thinking and doing to reestablish privacy-protecting rules-in-use.