The Perceived Effects of Media Exposure Scale (PEMES) assesses people’s beliefs about how their body image was impacted by exposure to specific media images (Frederick et al., 2017). It has been administered in experiments where participants are exposed to images of fashion models versus control images, and participants report whether the images had negative, neutral, or positive impacts on their feelings about different aspects of their appearance. It is was inspired by two previous scales that ask participants to assess the impact of pornography (Hald & Malamuth, 2008) and their body images (Body Image Quality of Life Inventory; Cash et al., 2004) on different aspects of their lives. The PEMES can be administered to adolescents and/or adults and is free to use. This chapter describes the development and psychometrics of the PEMES. The PEMES has been found to have a two-factor structure within two exploratory factor analyses: The PEMES-Weight subscale and PEMES-Face subscales. Internal reliability was high for both subscales. The chapter provides the PEMES items, responses scale, and scoring procedure. Logistics of use, such as permissions, copyright, and contact information, are provided for readers.

The Muscle Silhouette Measure (MSM) and Fat Silhouette Measure (FSM) are pictorial scales that assess perceptions of male body image (Frederick et al., 2007). They each contain eight images, with the MSM progressing from non-muscular to very muscular, and the FSM very low body fat to very high body fat. Consistent with self-discrepancy theory (Higgins, 1987), these measures assess men’s perceptions of their current bodies and their ideal bodies, and the discrepancy between these perceptions. It has also been used to assess women’s perceptions of the most attractive male body type and to code representations of muscularity level in popular magazines. The MSM and FSM can be administered to adolescents and/or adults and is free to use. This chapter describes the development and psychometrics of the MSM and FSM. The images were drawn by an artist based on photos of men in the Atlas of Men (Sheldon et al., 1954). Test-retest reliability was high for reports of current and ideal body. It was high for the MSM and moderate for the FSM for the self-ideal discrepancy. The chapter provides the images, response scale, and scoring procedure. Logistics of use, such as permissions, copyright, and contact information, are provided for readers.

The Body Matrices (Frederick & Peplau, 2007; Gray & Frederick, 2012) contain 28 computer-generated images of shirtless men varying in body fat and muscularity and 32 images of women in bikinis varying in body fat and breast size. The male matrices present seven levels of muscularity and four levels of body fat. The female matrices present eight levels of body fat and four levels of breast size. Each matrix contains all possible combinations of these body fat and muscularity/breast size level (e.g., slender women with large breasts; heavier women with smaller breasts). Consistent with self-discrepancy theory (Higgins, 1987), they assess men’s and women’s perceptions of their current bodies and their ideal bodies, and discrepancies between these perceptions. They can be administered to adolescents and/or adults and are free to use. This chapter describes the development and psychometrics of The Body Matrices. Test-retest reliability was high for current and ideal bodies for men and women, and moderate to high for self-ideal discrepancies. BMI (self-reported height and weight) was strongly associated with current body images chosen by participants. The chapter provides the images, response scale, and scoring procedure. Logistics of use, such as permissions, copyright, and contact information, are provided for readers.

When students are asked about the difficulties they experience in their epidemiology classes, one of the biggest barriers they report is the language their teachers use to describe the concepts being explained (note, it is the language rather than the concepts themselves). And here’s the thing: it is epidemiologists who are largely to blame, not the teachers! Being a relatively young discipline, it is not unusual to come across different words being used to describe the same concept, or the same word being used to describe different concepts – sometimes fundamentally different. Confusing, right?

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.