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Commercialization of research projects at the university, in particular, its efficiency and performance, have attracted little attention in the empirical literature to date. This despite the fact that commercialization of university knowledge is increasingly seen as a third task of universities and understanding of what enhances and what blocks the processes involved, is virtually lacking, particularly on the project level. The purpose of this chapter is therefore to identify factors that influence the performance of university-driven knowledge projects, including efficiency, in the context of commercialization of knowledge at universities. In this context, the study employs Data Envelop Analysis combined with Rough-Set Analysis on a sample of 42 projects in the Netherlands. The major factors influencing overall performance in commercialization turn out to be years of collaboration with large firms and efficiency in use of resources in the projects, but the affinity of the project managers at university with the market also plays a role. The best overall results in commercialization (introduction to market in a relatively short time) are gained with a longer period of collaboration with large firms (5–10 years) and a medium level of efficiency. There are also some contradictory trends. The chapter concludes with implications of the results, as well as some future research paths.

Using data from the General Social Survey (2002), structural equation modeling is employed to examine the intersections and relationships between various socio-demographic and contextual variables, patient trust, and patient preference for behaviors that indicate a desire to be an active health care participant. In so doing, a gap in the literature is addressed by uniting previous research on patient trust with research on patient participation. Findings reveal that patient trust in doctors and various socio-demographic and contextual variables are associated with people wanting to participate in the health care process by learning about medical issues on their own and by contributing to medical decisions. Results also shed new light on past research, which finds a relationship between various socio-demographic variables and patient trust. Specifically, they highlight the importance of distinguishing between patient trust in doctors and patient trust in the broader health care institution and the economic pressures it exerts on doctors. A discussion of what these findings might mean for our understanding of the doctor-patient relationship and the delivery of health care concludes the chapter.

The Whole Child Initiative (WCI) was developed over the past 15 years as a blueprint to promote long-term development and success of all children, as well as their families and communities. This chapter describes three aspects of the WCI model: (a) the need for a public health approach to sustainable, communitywide change targeting the whole child; (b) a clear, future-oriented vision for equipping educators, caregivers, and service providers with the skills and attitudes required to deliver high-quality instruction; and (c) the infusion of social and emotional learning practices to transform environments in which youth live and play. We provide examples of how schools, communities, and families can come together to create a common culture fostering stable and nurturing relationships essential for enhancing youth well-being. We close with recommended “super strategies” – low-cost, simple, and effective practices that can be broadly implemented to keep every child healthy, safe, engaged, supported, and challenged in the community at large.

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

Almost every detail of our lives – where we go, what we do, and with whom – is captured as digital data. Technological advancements in cloud computing, artificial intelligence, and data analytics offer the education sector new ways not only to improve policy and processes but also to personalize learning and teaching practice. However, these changes raise fundamental questions around who owns the data, how it might be used, and the consequences of use. The application of Big Data in education can be directed toward a wide range of stakeholders, such as educators, students, policy-makers, institutions, or researchers. It may also have different objectives, such as monitoring, student support, prediction, assessment, feedback, and personalization. This chapter presents the nuances and recent research trends spurred by technological advancements that have influenced the education sector and highlights the need to look beyond the technical boundaries using a socio-semiotic lens. With the explosion of available information and digital technologies pervading cultural, social, political as well as economic spaces, being a lifelong learner is pivotal for success. However, technology on its own is not sufficient to drive this change. For technology to be successful, it should complement individual learning cultures and education systems. This chapter is broadly divided into two main sections. In the first section, we contemplate a vision for the future, which is deemed possible based on ongoing digital and computing advancements. The second section elaborates the technological, pedagogical, cultural, and political requirements to attain that vision.

Academic outcomes for individuals with autism spectrum disorder (ASD) remain poor, especially in the area of reading, in particular, reading comprehension. In recent years, researchers have begun to investigate subcomponent skills of reading comprehension for children with ASD in order to better understand its development and potential interventions to enhance outcomes. This chapter highlights the current knowledge in the field in regards to the key cognitive and language skills associated with reading development for individuals with ASD. These include emergent-literacy skills, word-reading and decoding, reading fluency, oral language, and social cognition. Additionally, the chapter makes suggestions for future research in this area, in particular the need to conduct research to establish evidence-based practices to better support the syndrome-specific reading needs for this population.

Placing gifted and talented students together organizationally is not a substitute for appropriate services. The placement or program model fundamentally serves as a vehicle to group or organize students together but programming, in practice, sometimes referred to as a service delivery model, is not the same thing as service. Placement is a management strategy. It must be coupled with curriculum and instructional modifications in order for substantial and positive academic and social–emotional effects to occur for gifted and talented students. Specifically, the program placement model is only as good as the curriculum and instructional models provided within that placement. This chapter provides descriptions and research evidence of the macro program models used for serving gifted students and more commonly used program placement models for grouping gifted students together within the traditional school day and beyond. Non-negotiable components and future directions are also discussed within the context of placement.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.