In Quantified, Whitworth draws lessons from the world's most tech-savvy, high-impact organizations to show how we can make real gains for the environment. The principles of his approach, dubbed quantified conservation, will be familiar to any thriving entrepreneur: situational awareness, bold outcomes, innovation and technology, data and analytics, and gain-focused investment. As President of The Freshwater Trust, Whitworth has put quantified conservation into practice, pioneering the model of a "do-tank" that is dramatically changing how rivers can get restored across the United States. The stories in Quantified highlight the most precious of resources--water--but they apply to any environmental effort. Whether in the realm of policy, agriculture, business, or philanthropy, Whitworth is charting a new course for conservation.

Since 1986, the British government, faced with dwindling budgets and growing calls for public accountability, has sought to assess the value of scholarly work in the nation’s universities. Administrators have periodically evaluated the research of most full-time academics employed in British universities, seeking to distribute increasingly scarce funding to those who use it best. How do such attempts to quantify the worth of knowledge change the nature of scholarship? Juan Pablo Pardo-Guerra examines the effects of quantitative research evaluations on British social scientists, arguing that the mission to measure academic excellence resulted in less diversity and more disciplinary conformity. Combining interviews and original computational analyses, The Quantified Scholar provides a compelling account of how scores, metrics, and standardized research evaluations altered the incentives of scientists and administrators by rewarding forms of scholarship that were closer to established disciplinary canons. In doing so, research evaluations amplified publication hierarchies and long-standing forms of academic prestige to the detriment of diversity. Slowly but surely, they reshaped academic departments, the interests of scholars, the organization of disciplines, and the employment conditions of researchers. Critiquing the effects of quantification on the workplace, this book also presents alternatives to existing forms of evaluation, calling for new forms of vocational solidarity that can challenge entrenched inequality in academia.

What is at stake socially, culturally, politically, and economically when we routinely use technology to gather information about our bodies and environments? Today anyone can purchase technology that can track, quantify, and measure the body and its environment. Wearable or portable sensors detect heart rates, glucose levels, steps taken, water quality, genomes, and microbiomes, and turn them into electronic data. Is this phenomenon empowering, or a new form of social control? Who volunteers to enumerate bodily experiences, and who is forced to do so? Who interprets the resulting data? How does all this affect the relationship between medical practice and self care, between scientific and lay knowledge? Quantified examines these and other issues that arise when biosensing technologies become part of everyday life. The book offers a range of perspectives, with views from the social sciences, cultural studies, journalism, industry, and the nonprofit world. The contributors consider data, personhood, and the urge to self-quantify; legal, commercial, and medical issues, including privacy, the outsourcing of medical advice, and self-tracking as a “paraclinical” practice; and technical concerns, including interoperability, sociotechnical calibration, alternative views of data, and new space for design. Contributors Marc Böhlen, Geoffrey C. Bowker, Sophie Day, Anna de Paula Hanika, Deborah Estrin, Brittany Fiore-Gartland, Dana Greenfield, Judith Gregory, Mette Kragh-Furbo, Celia Lury, Adrian Mackenzie, Rajiv Mehta, Maggie Mort, Dawn Nafus, Gina Neff, Helen Nissenbaum, Heather Patterson, Celia Roberts, Jamie Sherman, Alex Taylor, Gary Wolf

This book raises questions about the changing relationships between technology, people and health. It examines the accelerating pace of technological development and a general shift to personalized, patient-led medicine. Such relationships are increasingly mediated through particular medical technologies, drawn together by the authors as ‘personal medical devices’ (PMDs) – devices that are attached to, worn by, interacted with, or carried by individuals for the purposes of generating biomedical data and carrying out medical interventions on the person concerned. The burgeoning PMD field is advancing rapidly across multiple domains and disciplines – so rapidly that conceptual and empirical research and thinking around PMDs, and their clinical, social and philosophical implications, often lag behind new technical developments and medical interventions. This timely and original volume explores the significant and under-researched impact of personal medical devices on contemporary understandings of health and illness. It will be a valuable read for scholars and practitioners of medicine, health, science and technology and social science.

The information revolution has ushered in a data-driven reorganization of the workplace. Big data and AI are used to surveil workers and shift risk. Workplace wellness programs appraise our health. Personality job tests calibrate our mental state. The monitoring of social media and surveillance of the workplace measure our social behavior. With rich historical sources and contemporary examples, The Quantified Worker explores how the workforce science of today goes far beyond increasing efficiency and threatens to erase individual personhood. With exhaustive detail, Ifeoma Ajunwa shows how different forms of worker quantification are enabled, facilitated, and driven by technological advances. Timely and eye-opening, The Quantified Worker advocates for changes in the law that will mitigate the ill effects of the modern workplace.

With the advent of digital devices and software, self-tracking practices have gained new adherents and have spread into a wide array of social domains. The Quantified Self movement has emerged to promote 'self-knowledge through numbers'. In this groundbreaking book Deborah Lupton critically analyses the social, cultural and political dimensions of contemporary self-tracking and identifies the concepts of selfhood and human embodiment and the value of the data that underpin them. The book incorporates discussion of the consolations and frustrations of self-tracking, as well as about the proliferating ways in which people's personal data are now used beyond their private rationales. Lupton outlines how the information that is generated through self-tracking is taken up and repurposed for commercial, governmental, managerial and research purposes. In the relationship between personal data practices and big data politics, the implications of self-tracking are becoming ever more crucial.

Quantification forms a significant aspect of cross-linguistic research into both sentence structure and meaning. This book surveys research in quantification starting with the foundational work in the 1970s. It paints a vivid picture of generalized quantifiers and Boolean semantics. It explains how the discovery of diverse scope behaviour in the 1990s transformed the view of quantification, and how the study of the internal composition of quantifiers has become central in recent years. It presents different approaches to the same problems, and links modern logic and formal semantics to advances in generative syntax. A unique feature of the book is that it systematically brings cross-linguistic data to bear on the theoretical issues, covering French, German, Dutch, Hungarian, Russian, Japanese, Telugu (Dravidian), and Shupamem (Grassfield Bantu) and points to formal semantic literature involving quantification in around thirty languages.

Quantifying the User Experience: Practical Statistics for User Research, Second Edition, provides practitioners and researchers with the information they need to confidently quantify, qualify, and justify their data. The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. It addresses questions users face every day, including, Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on their first attempt? How long will it take users to purchase products on the website? This book provides a foundation for statistical theories and the best practices needed to apply them. The authors draw on decades of statistical literature from human factors, industrial engineering, and psychology, as well as their own published research, providing both concrete solutions (Excel formulas and links to their own web-calculators), along with an engaging discussion on the statistical reasons why tests work and how to effectively communicate results. Throughout this new edition, users will find updates on standardized usability questionnaires, a new chapter on general linear modeling (correlation, regression, and analysis of variance), with updated examples and case studies throughout. - Completely updated to provide practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices - Includes new and revised information on standardized usability questionnaires - Includes a completely new chapter introducing correlation, regression, and analysis of variance - Shows practitioners which test to use, why they work, and best practices for application, along with easy-to-use Excel formulas and web-calculators for analyzing data - Recommends ways for researchers and practitioners to communicate results to stakeholders in plain English

This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.

Humans are accustomed to being tool bearers, but what happens when machines become tool bearers, calculating human labour via the use of big data and people analytics by metrics? The Quantified Self in Precarity highlights how, whether it be in insecure ‘gig’ work or office work, such digitalisation is not an inevitable process – nor is it one that necessarily improves working conditions. Indeed, through unique research and empirical data, Moore demonstrates how workplace quantification leads to high turnover rates, workplace rationalisation and worker stress and anxiety, with these issues linked to increased rates of subjective and objective precarity. Scientific management asked us to be efficient. Now, we are asked to be agile. But what does this mean for the everyday lives we lead? With a fresh perspective on how technology and the use of technology for management and self-management changes the ‘quantified’, precarious workplace today, The Quantified Self in Precarity will appeal to undergraduate and postgraduate students interested in fields such as Science and Technology, Organisation Management, Sociology and Politics.