This chapter describes the journey that the scientific assessment travels from author nomination through to the drafting and reviewing of the emerging report, and explores the social scientific order that structures and imprints on the IPCC’s writing of climate change through the process. It is in the initial stages of the scientific assessment, in the government nomination and author selection processes, that asymmetries in global knowledge of climate change and their effects become apparent. While developed countries have institutionalised processes for identifying and nominating experts, the majority of developing countries do not submit any author nominations. Once compiled, it is scientific conventions and measures of authority that are used to select and appoint the expertise necessary to fulfil the government approved outline of the report. However, when these activities are situated in broader patterns and practices of knowledge production, it becomes apparent that these reproduce the structures and exclusions of the existing global knowledge economy. These asymmetries are also apparent in the order of relations in the author teams and the submission of government review comments, which reduces the space for more diverse understandings and knowledges of climate change that are relevant to and reflect the interests and needs of all IPCC member governments. The IPCC has attempted to address these asymmetries through selection criteria and other mechanisms to shape the social order of authorship, which to date have proven more successful in broadening gender representation than ensuring the full participation of developing country authors in the assessment.

At the core of epidemiology is the use of quantitative methods to study health, and how it may be improved, in populations. It is important to note that epidemiology concerns not only the study of diseases but also of all health-related events. Rational health-promoting public policies require a sound understanding of causation. The epidemiological analysis of a disease or activity from a population perspective is vital in order to be able to organize and monitor effective preventive, curative and rehabilitative services. All health professionals and health-service managers need an awareness of the principles of epidemiology. They need to go beyond questions relating to individuals to challenging fundamentals such as ‘Why did this person get this disease at this time?’, ‘Is the occurrence of the disease increasing and, if so, why?’ and ‘What are the causes or risk factors for this disease?’

To effectively target public health interventions for greatest impact, it is essential that public health practitioners have a clear understanding of the populations they work with. As described in Chapter 1, understanding these populations, their health status and health needs draws on skills from several disciplines, including demography, epidemiology and statistics. Brought together, these skills allow the practitioner to understand the characteristics of the population of interest, the key health issues that it faces and the broader factors that have a particular influence on the health of that population. These broader factors are generally referred to as the wider determinants of health or the social determinants of health. Information is also vital in allowing practitioners to assess the impact of public health interventions.

This chapter explains why it is critical to measure health outcomes. It includes a review of the current measurement landscape in health care in the context of the Donabedian framework for assessing health care quality. It also reorients the reader to a focus on measuring outcomes and outlines why measuring outcomes can be challenging but must be done. The chapter also provides the reader with prompts for self-reflection on their outcome measurement aspirations and describes who the intended audience is for the guide.

We first show with proofs the basic and fundamental concepts and theorems from abstract and geometric measure theory. These include, in particular, the three classical covering theorems: 4r, Besicovitch, and Vitali type. We also include a short section on probability theory: conditional expectations and Martingale Theorems. We devote quite a significant amount of space to treating Hausdorff and packing measures. In particular, we formulate and prove Frostman Converse Lemmas, which form an indispensable tool for proving that a Hausdorff or packing measure is finite, positive, or infinite. Some of these are frequently called, in particular in the fractal geometry literature, the mass redistribution principle, but these lemmas involve no mass redistribution. We then deal with Hausdorff, packing, box counting, and dimensions of sets and measures, and provide tools to calculate and estimate them.

Chapter 1 sets out the conceptual framework through which the book examines research evaluation and names the key players and processes involved. It begins by outlining The Evaluation Game’s key contention that research evaluation is a manifestation of a broader technology which the book refers to as “evaluative power.” Next, it describes how the evaluative power comes to be legitimized and how it introduces one of its main technologies: research evaluation systems. The chapter then defines games as top-down social practices and, on the basis of this conceptual framework, presents the evaluation game as a reaction to or resistance against the evaluative power. Overall, the chapter shows how the evaluation of both institutions and knowledge produced by researchers working in them have, unavoidably, become an integral element of the research process itself.

This chapter focuses on the complexities of systems and selves as they pertain to critical consciousness scholarship. By systems, we mean the multiple and intersecting systems of oppression that historically and currently operate in the United States and globally. By selves, we mean individuals’ understandings of themselves and their social locations. Our chapter in organized in three sections: (1) the complexity of systems, (2) the complexity of selves, and (3) the complexity of relationships between systems and selves. Within each section, we highlight several points about complexity, share some observations about how that complexity has been addressed within quantitative critical consciousness scholarship, and make practical recommendations for quantitative research specifically (and researchers across fields and methodological orientations more generally). We suggest that attending to these complexities will enhance scholarship and contribute to the knowledge base about how to promote socially just actions in different groups of young people and in different contexts.

Historical background and some milestones

Chapter 1 explains the function of Article 1 as the threshold provision defining the applicability of the Agreement on Safeguards. It explains what safeguard measures are for purposes of WTO law.

This chapter explains how the collected data can be processed with the aim of extracting meaningful measures, and how statistical analysis can be used to support significant conclusions. This chapter first introduces common quantitative measures for transparency research, including measures for tracking, privacy, fairness, and similarity. To compute most of these measures, data need to be preprocessed to extract the response variables of interest from the raw collected data, for example using simple transformations or heuristics, machine learning classifiers or natural language processing, or static and dynamic analysis methods for mobile apps. Finally, the chapter explains statistical methods that allow to make meaningful and statistically significant statements about the behavior of the response variables in the experiment.