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.

Personal information is inherently about someone, is often shared unintentionally or involuntarily, flows via commercial communication infrastructure, and can be instrumental and often essential to building trust among members of a community. As a result, privacy commons governance may be ineffective, illegitimate, or both if it does not appropriately account for the interests of information subjects or if infrastructure is owned and designed by actors whose interests may be misaligned or in conflict with the interests of information subjects. Additional newly emerging themes include the importance of trust; the contestability of commons governance legitimacy; and the co-emergence of contributor communities and knowledge resources. The contributions in this volume also confirm and deepen insights into recurring themes identified in previous GKC studies, while the distinctive characteristics of personal information add nuance and uncover limitations. The studies in this volume move us significantly forward in our understanding of knowledge commons, while opening up important new directions for future research and policy development, as discussed in this concluding chapter.

This introduction to Governing Privacy in Knowledge Commons discusses how meta-analysis of past case studies has yielded additional questions to supplement the GKC framework, based on the specific governance challenges around personal information. Based on this renewed understanding, a series of new case studies are organized around the different roles that personal information play in commons arrangements. The knowledge commons perspective highlights the interdependence between knowledge flows aimed at creative production and personal information flows. Madelyn will discuss how those who systematically study knowledge commons governance with an eye toward knowledge production routinely encounter privacy concerns and values, along with rules in use that govern appropriate personal information flow.

Drawing upon the GKC framework, this chapter presents an ethnographic study of Woebot – a therapy chatbot designed to administer a form of cognitive behavioral therapy (“CBT”). Section 3.1 explains the methodology of this case study. Section 3.2 describes the background contexts that relate to anxiety as a public health problem. These include the nature of anxiety and historical approaches to diagnosing and treating it, the ascendency of e-Mental Health therapy provided through apps, and relevant laws and regulations. Section 3.3 describes how Woebot was developed and what goals its designers pursued. Section 3.4 describes the kinds of information that users share with Woebot. Section 3.5 describes how the designers of the system seek to manage this information in a way that benefits users without disrupting their privacy.

The random impact rule allows us to build a null model of a career. Using the null model, we can examine what a scientific career looks like when its driven by chance alone. We call this the R-model. But the R-model only accounts for differences in productivity, not differences in ability or talent. In response to this discrepancy, we create the Q-model, which assumes that the impact of papers we publish is determined by two factors, luck and a Q parameter unique to each person. We can then calculate how a person’s highest impact paper is expected to change with productivity. We find that the Q-model’s predictions are in excellent agreement with real world data. In fact, the Q parameter alone seems sufficient to explain what differentiates one scientist from another. We also find that it remains relatively stable over the course of a career. We then show how we calculate the Q factor for individual scientists, how we can use their Q factor to predict their impact, and how doing so provides a more accurate forecast of a scientist’s future impact than the h-index can. In the case of great scientists, we see that the Q factor turns luck into a consistently high impact career.

We begin by detailing Einstein’s “miracle year,” during which he published four major discoveries. We discuss the debate over the existence of hot streaks in sports, and ask if a hot-streak phenomenon exists in science. To address this question, we look at the relative timing of hit works during a career, which is captured mathematically by a normalized joint probability. We find that hit works are more likely to colocate than they would by chance, indicating that hot streaks do occur. So while the timing of each highest impact work is random, the relative timing of top papers in a scientist’s career shows a high degree of temporal clustering. To account for these patterns, we introduce a slight variation to the Q-model – a brief period of elevated impact. We call this the “hot-streak model.” The model shows us that hot streaks are ubiquitous across creative careers, that they usually occur only once, and that they occur randomly. We then discuss the implications of these findings for scientists and science administrators, using the life of John Fenn as an example of how the hot-streak model can provide a hopeful framework for scientists still waiting for their big break.

We illustrate some of the challenges of credit allocation in science by discussing the Thomas theorem –often seen as the orgin of the “self-fulfilling prophecy” – which, ironically given its subject matter, has been repeatedly cited as the work of W. I. Thomas alone. Thomas’ coauthor and wife, Dorothy Swaine Thomas, has never received the credit she deserved for the discovery. This raises this issue of how biases affect credit allocation in science, since our perception of who deserves credit is reinforced by the Matthew effect. We tend to give disproportionate credit to renowned scientists over unknowns, making coauthoring with eminent scientists risky. Many of these problems arise because credit is allocated collectively in science, based on the community’s perception of who is responsible for a discovery. While that perception is often correct, there are plenty of instances where the community gets it wrong. We describe how a collective credit allocation algorithm, which was created using cocitation patterns, can capture how the community assigns credit and predict who will get credit for a discovery. We then discuss the algorithm’s implications for individual scientists.