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Internetnutzung im häuslichen Alltag: Räumliche Arrangements zwischen Fragmentierung und Gemeinschaft (essentials)
by Jutta Röser Corinna PeilDer Beitrag präsentiert aktuelle Befunde zur Internetnutzung im häuslichen Alltag. Die Autorinnen nehmen zunächst eine Systematisierung alltagsbezogener Rezeptionsforschung der Cultural Studies vor und führen den Domestizierungsansatz ein. Auf Basis ethnografisch orientierter Haushaltsstudien wird anschließend aufgezeigt, auf welche Weise Internetnutzung, räumliche Arrangements und häusliche Kommunikationsstrukturen miteinander interagieren. Abschließend werden verschiedene Arrangements beschrieben und deren Einflüsse auf die Herstellung von Gemeinschaft und Fragmentierung, auf geschlechtsgebundene Praktiken sowie auf Funktionen anderer Medien, insbesondere des Fernsehens, skizziert.
The Internet's Coming Of Age
by Compature Science Telecommunications Board National Research CouncilWhat most of us know as "the Internet" is actually a set of largely autonomous, loosely coordinated communication networks. As the influence of the Internet continues to grow, understanding its real nature is imperative to acting on a wide range of policy issues.This timely new book explains basic design choices that underlie the Internet's success, identifies key trends in the evolution of the Internet, evaluates current and prospective technical, operational, and management challenges, and explores the resulting implications for decision makers. The committee-composed of distinguished leaders from both the corporate and academic community-makes recommendations aimed at policy makers, industry, and researchers, going on to discuss a variety of issues: How the Internet's constituent parts are interlinked, and how economic and technical factors make maintaining the Internet's seamless appearance complicated. How the Internet faces scaling challenges as it grows to meet the demands of users in the future. Tensions inherent between open innovation on the Internet and the ability of innovators to capture the commercial value of their breakthroughs. Regulatory issues posed by the Internet's entry into other sectors, such as telephony.
Internetware
by Hong Mei Jian LüThis book presents a comprehensive introduction to Internetware, covering aspects ranging from the fundamental principles and engineering methodologies to operational platforms, quality measurements and assurance and future directions. It also includes guidelines and numerous representative real-world case studies that serve as an invaluable reference resource for software engineers involved in the development of Internetware applications. Providing a detailed analysis of current trends in modern software engineering in the Internet, it offers an essential blueprint and an important contribution to the research on software engineering and systems for future Internet computing.
Internetwirtschaft
by Falk Von Bornstaedt Rüdiger Zarnekow Jochen WulfWährend der Aufbau und Betrieb von Internetinfrastrukturen zur Zeit der Entstehung des Internets in erster Linie von öffentlichen Institutionen vorangetrieben wurde, wird dieser Bereich heute längst von privatwirtschaftlichen Unternehmen beherrscht. Dieses Buch beschreibt Dienste, Wertschöpfungsprozesse und Wettbewerbsstrategien zur Erbringung des Datentransports im Internet aus betriebswirtschaftlicher Sicht. Es werden gleichermaßen Internetzugangsdienste zur Anbindung von Endkunden, Transitdienste zum Datenaustausch zwischen Netzbetreibern, Dienste zur Kapazitätsbereitstellung im Kernnetz und Dienste zur Distribution digitaler Inhalte und Anwendungen diskutiert. Durch die Darstellung der Teilmärkte, die den Datentransport im Internet adressieren, wird dem Leser ein umfassender Einblick in die Internetwirtschaft geboten, ohne dass hierzu ein tiefer gehendes technisches Verständnis vorausgesetzt wird. Darüber hinaus werden strategische Herausforderungen bei der Bereitstellung digitaler Inhalte und Anwendungen vorgestellt, die zukünftige Entwicklungen in der Internetwirtschaft maßgeblich beeinflussen.
Interoperability and Open-Source Solutions for the Internet of Things
by Ivana Podnar Žarko Arne Broering Sergios Soursos Martin SerranoThis book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Interoperability and Open-Source Solutions for the Internet of Things, FP7 OpenIot Project, held in Conjunction with SoftCOM 2014, in Split, Croatia, in September 2014. The 11 revised full papers presented together with the extended abstracts of 2 keynote talks were carefully reviewed and selected from numerous submissions during two rounds of reviewing and improvement. The papers are organized in topical sections on OpenIoT platform, open platforms and standards, and IoT Applications.
Interoperability and Open-Source Solutions for the Internet of Things
by Ivana Podnar Žarko Krešimir Pripužić Martin SerranoThis book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Interoperability and Open-Source Solutions for the Internet of Things, FP7 OpenIot Project, held in Conjunction with SoftCOM 2014, in Split, Croatia, in September 2014. The 11 revised full papers presented together with the extended abstracts of 2 keynote talks were carefully reviewed and selected from numerous submissions during two rounds of reviewing and improvement. The papers are organized in topical sections on OpenIoT platform, open platforms and standards, and IoT Applications.
Interoperability for Enterprise Software and Applications: Proceedings of the Workshops and the Doctorial Symposium of the I-ESA International Conference 2010
by Hervé Panetto Nacer BoudjlidaWithin the framework of the Sixth I-ESA International Conference, supported by the INTEROP VLab (International Virtual Laboratory on Enterprise Interoperability, http://www.interop-vlab.eu), three workshops and a Doctoral Symposium have been organized in order to strengthen some key topics related to interoperability for enterprise applications and software. The workshops were selected to complement the conference topics, leaving more time to researchers for brainstorming and then coming up, at the end of the workshops, with new research directions for the future. The goal of the workshop “Standards – a Foundation for Interoperability” is to increase awareness and understanding of interoperability standards as a fundamental need. The workshop “Use of MDI/SOA Concepts in Industry” promotes the application of MDI (Model-Driven Interoperability) combined with SOA (Services Oriented Architecture) and the associated technology (BPM, Enterprise Modeling, ontology, mediation, model transformation, etc.) in industry. The workshop on “Dynamic Management across Interoperating Enterprises” investigates the need for enhancements to current business management systems and processes to address the needs of global trading across enterprises utilizing the new service-oriented Internet. Finally, the Doctoral Symposium has given the opportunity for students involved in the preparation of their PhDs in this emerging area to present and discuss their research issues and ideas with senior researchers.
Interoperability in IoT for Smart Systems (Intelligent Systems)
by Monideepa Roy Pushpendu Kar Sujoy DattaInteroperability in IoT for Smart Systems discusses the different facets of interoperability issues among the IoT devices and their solutions, the scalability issues in an IoT network, and provides solutions for plug-n-play of new devices with the existing IoT system. It also addresses the possible usage of interoperable and plug-n-play IoT networks in different systems to make them smarter. Aimed at researchers and graduate students in computer science, computer engineering, computer networks, electronics engineering, this book Exclusively covers interoperability of IoT systems in parallel with their use towards the development of smart systems Discusses the requirements of interoperability in smart IoT systems and their solutions Reviews IoT applications in different smart and intelligent systems Explores dealing with interoperability of heterogeneous participating devices Provides different case studies and open problems related to interoperability in IoT systems
Interoperability of Heterogeneous IoT Platforms: A Layered Approach (Internet of Things)
by Carlos E. Palau Giancarlo Fortino Miguel Montesinos George Exarchakos Pablo Giménez Garik Markarian Valérie Castay Flavio Fuart Wiesław Pawłowski Marina Mortara Alessandro Bassi Frans Gevers Gema Ibáñez-Sánchez Ignacio HuetThis book discusses the design and implementation of, as well as experimentation on, an open cross-layer framework and associated methodology to provide voluntary interoperability among heterogeneous Internet of Things (IoT) platforms. It allows readers to effectively and efficiently develop smart IoT applications for various heterogeneous IoT platforms, spanning single and/or multiple application domains. To do so, it provides an interoperable framework architecture for the seamless integration of different IoT architectures present in different application domains. In this regard, interoperability is pursued at various levels: device, network, middleware, services and data.
Interoperability, Safety and Security in IoT: Third International Conference, InterIoT 2017, and Fourth International Conference, SaSeIot 2017, Valencia, Spain, November 6-7, 2017, Proceedings (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering #242)
by Giancarlo Fortino Carlos E. Palau Antonio Guerrieri Nora Cuppens Frédéric Cuppens Hakima Chaouchi Alban GabillonThis book constitutes the refereed post-conference proceedings of the Third International Conference on Interoperability, InterIoT 2017, which was collocated with SaSeIoT 2017, and took place in Valencia, Spain, in November 2017. The 14 revised full papers were carefully reviewed and selected from 22 submissions and cover all aspects of the latest research findings in the area of Internet of Things (IoT).
Interoperability, Safety and Security in IoT
by Nathalie Mitton Hakima Chaouchi Thomas Noel Thomas Watteyne Alban Gabillon Patrick CapolsiniThis book constitutes the refereed post-conference proceedings of the International Conference on Safety and Security in Internet of Things , SaSeIoT 2016, which was collocated with InterIoT and took place in Paris, France, in October 2016. The 14 revised full papers were carefully reviewed and selected from 22 submissions and cover all aspects of the latest research findings in the area of Internet of Things (IoT).
Interoperable Electronic Safety Equipment: Performance Requirements for Compatible and Interoperable Electronic Equipment for Emergency First Responders
by Casey C GrantFirefighters and other emergency first responders use a huge variety of highly specialized and critical technologies for personal protection. These technologies, ranging from GPS to environmental sensing to communication devices, often run on different systems with separate power supplies and operating platforms. How these technological components function in a single synergistic system is of critical interest to firefighter end-users seeking efficient tools. Interoperable ESE states that a standardized platform for electronic safety equipment (ESE) is both logical and essential. This book develops an inventory of existing and emerging electronic equipment categorized by key areas of interest to the fire service, documents equipment performance requirements relevant to interoperability, including communications and power requirements, and develops an action plan toward the development of requirements to meet the needs of emergency responders. This book is intended for practitioners as a tool for understanding interoperability concepts and the requirements of the fire service landscape. It offers clear recommendations for the future to help ensure efficiency and safety with fire protection equipment. Researchers working in a related field will also find the book valuable.
Interpersonal Interactions and Language Learning: Face-to-Face vs. Computer-Mediated Communication
by Shin Yi Chew Lee Luan NgThis book takes as its starting point the assumption that interpersonal communication is a crucial aspect of successful language learning. Following an examination of different communicative models, the authors focus on traditional face-to-face (F2F) interactions, before going on to compare these with the forms of computer-mediated communication (CMC) enabled by recent developments in educational technology. They also address the question of individual differences, particularly learners' preferred participation styles, and explore how F2F and CMC formats might impact learners differently. This book will be of interest to students and scholars of computer-mediated communication (CMC), computer-assisted language learning (CALL), technology-enhanced language learning (TELL), language acquisition and language education more broadly.
The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings Of The 3rd International Conference On Smart Learning Ecosystems And Regional Development (Smart Innovation, Systems And Technologies #95)
by Antonio Cartelli Elvira Popescu Hendrik KnocheThis book gathers contributions to the 3rd International Conference on Smart Learning Ecosystems and Regional Developments (SLERD 2018), held at Aalborg University, Denmark on 23–25 May 2018. What characterizes smart learning ecosystems? What is their role in city and regional development and innovation? How can we promote citizen engagement in smart learning ecosystems? These are some of the questions addressed at SLERD 2018 and documented in these proceedings, which include a diverse range of papers intended to help understand, conceive, and promote innovative human-centric design and development methods, education/training practices, informal social learning, and citizen-driven policies. The papers elaborate on the notion of smart learning ecosystems, assess the relation of smart learning ecosystems with their physical surroundings, and identify new resources for smart learning. SLERD 2018 contributes to foster the social innovation sectors, ICT and economic development and deployment strategies, as well as new policies for smarter, more proactive citizens. As such, these proceedings are relevant for researchers and policymakers alike.
Interpretability in Deep Learning
by Ayush Somani Alexander Horsch Dilip K. PrasadThis book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.
Interpretability of Computational Intelligence-Based Regression Models
by János Abonyi Tamás KeneseiThe key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression. The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.
Interpretability of Machine Intelligence in Medical Image Computing: 5th International Workshop, iMIMIC 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13611)
by Mauricio Reyes Pedro Henriques Abreu Jaime CardosoThis book constitutes the refereed joint proceedings of the 5th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2022, held in September 2022, in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022.The 10 full papers presented at iMIMIC 2022 were carefully reviewed and selected from 24 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention.
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support: Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (Lecture Notes in Computer Science #11797)
by Ben Glocker Kenji Suzuki Mauricio Reyes Tanveer Syeda-Mahmood Hayit Greenspan Anant Madabhushi Roland Wiest Eth Zurich Yaniv GurThis book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings (Lecture Notes in Computer Science #12929)
by Mauricio Reyes Pedro Henriques Abreu Jaime Cardoso Mustafa Hajij Ghada Zamzmi Paul Rahul Lokendra ThakurThis book constitutes the refereed joint proceedings of the 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, and the First International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021, held on September 27, 2021, in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021.The 7 full papers presented at iMIMIC 2021 and 5 full papers held at TDA4MedicalData 2021 were carefully reviewed and selected from 12 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. TDA4MedicalData is focusing on using TDA techniques to enhance the performance, generalizability, efficiency, and explainability of the current methods applied to medical data.
Interpretable AI: Building explainable machine learning systems
by Ajay ThampiAI doesn&’t have to be a black box. These practical techniques help shine a light on your model&’s mysterious inner workings. Make your AI more transparent, and you&’ll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements.In Interpretable AI, you will learn: Why AI models are hard to interpret Interpreting white box models such as linear regression, decision trees, and generalized additive models Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning What fairness is and how to mitigate bias in AI systems Implement robust AI systems that are GDPR-compliant Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You&’ll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model. About the technology It&’s often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside &“black box&” models, designing accountable algorithms, and understanding the factors that cause skewed results. About the book Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you&’ll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You&’ll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python. What's inside Techniques for interpreting AI models Counteract errors from bias, data leakage, and concept drift Measuring fairness and mitigating bias Building GDPR-compliant AI systems About the reader For data scientists and engineers familiar with Python and machine learning. About the author Ajay Thampi is a machine learning engineer focused on responsible AI and fairness. Table of Contents PART 1 INTERPRETABILITY BASICS 1 Introduction 2 White-box models PART 2 INTERPRETING MODEL PROCESSING 3 Model-agnostic methods: Global interpretability 4 Model-agnostic methods: Local interpretability 5 Saliency mapping PART 3 INTERPRETING MODEL REPRESENTATIONS 6 Understanding layers and units 7 Understanding semantic similarity PART 4 FAIRNESS AND BIAS 8 Fairness and mitigating bias 9 Path to explainable AI
Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings (Lecture Notes in Computer Science #12446)
by Jaime Cardoso Emanuele Trucco Diana Mateus Veronika Cheplygina Pedro Henriques Abreu Raphael Sznitman Nicholas Heller Steve Jiang Vishal Patel Badri Roysam Kevin Zhou Khoa Luu Ngan Le Hien Van Nguyen Ivana Isgum Wilson Silva Ricardo Cruz Jose Pereira Amorim Samaneh AbbasiThis book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.
Interpretable Artificial Intelligence: A Perspective of Granular Computing (Studies in Computational Intelligence #937)
by Witold Pedrycz Shyi-Ming ChenThis book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) – Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.
Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples
by Serg MasisUnderstand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable modelsKey FeaturesLearn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.What you will learnRecognize the importance of interpretability in businessStudy models that are intrinsically interpretable such as linear models, decision trees, and Naive BayesBecome well-versed in interpreting models with model-agnostic methodsVisualize how an image classifier works and what it learnsUnderstand how to mitigate the influence of bias in datasetsDiscover how to make models more reliable with adversarial robustnessUse monotonic constraints to make fairer and safer modelsWho this book is forThis book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Interpretable Three-Way Decision with Hesitant Risk Information and Its Healthcare Application (Studies in Fuzziness and Soft Computing #431)
by Decui Liang Zeshui XuAs a new interpretable model, three-way decision has also received academic attention in machine learning. With respect to different hesitant fuzzy information, this book deeply discusses the deduction process of decision rules of three-way decision and generates interpretable knowledge with the risk semantics. It further explores the applications of three-way decision to support healthcare management. This book is used as a reference for engineers, technicians, and researchers who are working in the fields of management science, operation management, computer science, information management, fuzzy mathematics, business intelligence, and other fields. It also serves as a textbook for postgraduate and senior undergraduate students of the relevant professional institutions of higher learning.
Interpreting Hashtag Politics
by Stephen JeffaresWhy do policy actors create branded policy ideas like Big Society and does launching them on Twitter extend or curtail their life? This book reveals how policy analysis can adapt in an increasingly mediatised to offer interpretive insights into the life and death of policy ideas in an era of hashtag politics.