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An important and timely plenary session at the 2015 UKSG Conference and Exhibition focused on the role of metrics in research assessment. The two excellent speakers had slightly divergent views.Todd Carpenter from NISO (National Information Standards Organization) argued that altmetrics aren't alt anymore and that downloads and other forms of digital interaction, including social media reference, reference tracking, personal library saving, and secondary linking activity now provide mainstream approaches to the assessment of scholarly impact. James Wilsdon is professor of science and democracy in the Science Policy Research Unit at the University of Sussex and is chair of the Independent Review of the Role of Metrics in Research Assessment commissioned by the Higher Education Funding Council in England (HEFCE). The outcome of this review will inform the work of HEFCE and the other UK higher education funding bodies as they prepare for the future of the Research Excellence Framework. He is more circumspect arguing that metrics cannot and should not be used as a substitute for informed judgement. This article provides a summary of both presentations.

Predictive algorithms are increasingly being deployed in a variety of settings to determine legal status. Algorithmic predictions have been used to determine provision of health care and social services, to allocate state resources, and to anticipate criminal behavior or activity. Further applications have been proposed to determine civil and criminal liability or to "personalize"legal default rules. Deployment of such artificial intelligence (AI) systems has properly raised questions of algorithmic bias, fairness, transparency, and due process. But little attention has been paid to the known sociological costs of using predictive algorithms to determine legal status. A large and growing social science literature teaches the effects of "algorithmic living,"documenting how humans interact with machine generated assessments. Many of these interactions are socially detrimental, and such corrosive effects are greatly amplified by the increasing speed and ubiquity of digitally automated algorithmic systems. In this Article I link the sociological and legal analysis of AI, highlighting the reflexive social processes that are engaged by algorithmic metrics. This Article examines these overlooked social effects of predictive legal algorithms and contributes to the literature a vital fundamental but missing critique of such analytics. Specifically, this Article shows how the problematic social effects of algorithmic legal metrics extend far beyond the concerns about accuracy that have thus far dominated critiques of such metrics. Second, it demonstrates that corrective governance mechanisms such as enhanced due process or transparency will be inadequate to remedy such corrosive effects, and that some such remedies, such as transparency, may actually exacerbate the worst effects of algorithmic governmentality. Third, the Article shows that the application of algorithmic metrics to legal decisions aggravates the latent tensions between equity and autonomy in liberal institutions, undermining democratic values in a manner and on a scale not previously experienced by human societies. Illuminating these effects casts new light on the inherent social costs of AI metrics, particularly the perverse effects of deploying algorithms in legal systems.

Predictive algorithms are increasingly being deployed in a variety of settings to determine legal status. Algorithmic predictions have been used to determine provision of health care and social services, to allocate state resources, and to anticipate criminal behavior or activity. Further applications have been proposed to determine civil and criminal liability or to "personalize" legal default rules. Deployment of such artificial intelligence systems has properly raised questions of algorithmic bias, fairness, transparency, and due process. But little attention has been paid to the known sociological costs of using predictive algorithms to determine legal status. A large and growing social science literature teaches the effects of "algorithmic living," documenting how humans interact with machine generated assessments. Many of these interactions are socially detrimental, and such corrosive effects are greatly amplified by the increasing speed and ubiquity of digitally automated algorithmic systems. In this paper I link the sociological and legal analysis of AI, highlighting the reflexive social processes that are engaged by algorithmic metrics. This paper examines these overlooked social effects of predictive legal algorithms, and contributes to the literature a vital fundamental but missing critique of such analytics. Specifically, this paper shows how the problematic social effects of algorithmic legal metrics extend far beyond the concerns about accuracy that have thus far dominated critiques of such metrics. Second, it demonstrates that corrective governance mechanisms such as enhanced due process or transparency will be inadequate to remedy such corrosive effects, and that some such remedies, such as transparency, may actually exacerbate the worst effects of algorithmic governmentality. Third, the paper shows that the application of algorithmic metrics to legal decisions aggravates the latent tensions between equity and autonomy in liberal institutions, undermining democratic values in a manner and on a scale not previously experienced by human societies. Illuminating these effects casts new light on the inherent social costs of AI metrics, particularly the perverse effects of deploying algorithms in legal systems.

Versification describes the marriage of language and poetic form through which poetry is produced. Formal principles, such as metre, alliteration, rhyme, or parallelism, take precedence over syntax and prosody, resulting in expressions becoming organised as verse rather than prose. The aesthetic appeal of poetry is often linked to the potential for this process to seem mysterious or almost magical, not to mention the interplay of particular expressions with forms and expectations. The dynamics of versification thus draw a general interest for everyone, from enthusiasts of poetry or forms of verbal art to researchers of folklore, ethnomusicology, linguistics, literature, philology, and more. The authors of the works in the present volume explore versification from a variety of angles and in diverse cultural milieus. The focus is on metrics in practice, meaning that the authors concentrate not so much on the analysis of the metrical systems per se as on the ways that metres are used and varied in performance by individual poets and in relationship to language.

An added benefit of doing library instruction is you learn things from students and faculty. This knowledge informs both collection development and research consultations. It is especially interesting when a new faculty member arrives and issues a revised syllabus for a popular course. One such class at UC Berkeley is in the Global Poverty and Practice (GPP) minor, founded by Professor Ananya Roy ten years ago. Her book, Poverty Capital: Microfinance and the Making of Development, makes the uncomfortable point that people and institutions profit from poverty: it is a lucrative business. But there are also those who attempt to create and influence "poverty knowledge." The 1998 subtitle of the World Bank's flagship publication, the World Development Report, was "Knowledge for Development." In 2017 the World Bank wrote a feature news article (about itself) as a "knowledge institution." There are articles that trace the history of the World Bank's vision of itself as a "knowledge bank," a term I find both amusing (do they charge "interest"?) and problematic. Yet a library is also a knowledge institution, and what we purchase or recommend influences the thinking and research of students and scholars.

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"It's often said that people cannot manage what they cannot measure. Marketing Value Metrics shows not only how marketing systematically contributes to shareholder value, but also provides a framework for developing and implementing marketing strategies that are measurable and accountable. Building on the concepts and research found in Marketing Accountability written the same authors, Marketing Value Metrics is fully up-to-date to include analytics pertinent for today's marketers such as social media metrics. The key steps in the modeling process are described in detail, as are the procedures for applying it in practice.Marketing Value Metrics enables senior executives to effectively measure the impact of marketing activity against organizational goals, and will empower marketing teams and their managers to justify and defend their plans and strategies to their CEOs and CFOs"--

[EN] In 1994, Matthews introduced the notion of partial metric and established a duality relationship between partial metrics and quasi-metrics defined on a set X. In this paper, we adapt such a relationship to the fuzzy context, in the sense of George and Veeramani, by establishing a duality relationship between fuzzy quasi-metrics and fuzzy partial metrics on a set X, defined using the residuum operator of a continuous t-norm *. Concretely, we provide a method to construct a fuzzy quasi-metric from a fuzzy partial one. Subsequently, we introduce the notion of fuzzy weighted quasi-metric and obtain a way to construct a fuzzy partial metric from a fuzzy weighted quasi-metric. Such constructions are restricted to the case in which the continuous t-norm * is Archimedean and we show that such a restriction cannot be deleted. Moreover, in both cases, the topology is preserved, i.e., the topology of the fuzzy quasi-metric obtained coincides with the topology of the fuzzy partial metric from which it is constructed and vice versa. Besides, different examples to illustrate the exposed theory are provided, which, in addition, show the consistence of our constructions comparing it with the classical duality relationship. ; Juan-Jose Minana acknowledges financial support from FEDER/Ministerio de Ciencia, Innovacion y Universidades-Agencia Estatal de Investigacion/Proyecto PGC2018-095709-B-C21, and by Spanish Ministry of Economy and Competitiveness under contract DPI2017-86372-C3-3-R (AEI, FEDER, UE). This work is also partially supported by Programa Operatiu FEDER 2014-2020 de les Illes Balears, by project PROCOE/4/2017 (Direccio General d'Innovacio i Recerca, Govern de les Illes Balears) and by projects ROBINS and BUGWRIGHT2. These two latest projects have received funding from the European Union's Horizon 2020 research and innovation program under grant agreements No 779776 and No 871260, respectively. This publication reflects only the authors views and the European Union is not liable for any use that may be made of ...

Invaluable advice on analyzing and measuring the effects of social media Do you wish you could sit down with an expert to figure out whether or not your social media initiatives are working? With Social Media Metrics Secrets, you can! Expert John Lovett taps into his years of training and experience to reveal tips, tricks, and advice on how to analyze and measure the effects of social media and gauge the success of your initiatives. He uses mini case studies to demonstrate how to manage social operations with process and technology by applying key performance indicators, and assessing the busi