Stefaan Verhulst
Judicaël Picaut at al at Building and Environment: “An alternative method is proposed for the assessment of the noise environment, on the basis of a crowdsourcing approach. For this purpose, a smartphone application and a spatial data infrastructure have been specifically developed in order to collect physical data (noise indicators, GPS positions, etc.) and perceptual data (pleasantness), without territorial limits, of the sound environment.
As the project is developed within an Open Science framework, all source codes, methodologies, tools and raw data are freely available, and if necessary, can be duplicated for any specific use. In particular, the collected data can be used by the scientific community, cities, associations, or any institution, which would like to develop new tools for the evaluation and representation of sound environments. In this paper, all the methodological and technical issues are detailed, and a first analysis of the collected data is proposed….(More)”.
Oxford Dictionaries: “Folksonomy, a portmanteau word for ‘folk taxonomy’, is a term for collaborative tagging: the production of user-created ‘tags’ on social media that help readers to find and sort content. In other words, hashtags: #ThrowbackThursday, #DogLife, #MeToo. Because ordinary people create folksonomy tags, folksonomies include categories devised by small communities, subcultures, or even individuals, not merely those by accepted taxonomic systems like the Dewey Decimal System.
The term first arose in the wake of Web 2.0 – the Web’s transition, in the early 2000s, from a read-only platform to a read-write platform that allows users to comment on and collaboratively tag what they read. Rather unusually, we know the exact date it was coined: 24 July, 2004. The information architect Thomas Vander Wal came up with it in response to a query over what to call this kind of informal social classification.
Perhaps the most visible folksonomies are those on social-media platforms like Facebook, Twitter, Tumblr, Flickr, and Instagram. Often, people create tags on these platforms in order to gather under a single tag content that many different users have created, making it easier to find posts related to that tag. (If I’m interested in dogs, I might look at content gathered under the tag #DogLife.) Because tags reflect the interests of people who create them, researchers have pursued ways to use tags to build more comprehensive profiles of users, with an eye to surveillance or to selling them relevant ads.
But people may also use tags as prompts for the creation of new content, not merely the curation of content they would have posted anyway. As I write this post, a trending tag on Twitter, #MakeAHorrorMovieMoreHorrific, is prompting thousands of people to write satirical takes on how classic horror movies might be made more ‘horrifying’ by adding unhappy features of our ordinary lives. (‘I Know What You Did Last Summer, and I Put It on Facebook’; ‘Rosemary’s Baby Is Teething’; ‘The Exercise’)
From a certain perspective, this is not so different from a library’s acknowledgment of a new category of text: if a new academic field, like ‘the history of the book’, catches on, then libraries rearrange their shelves and catalogues to accommodate the history of the book as a category; the new shelf space and catalogue space creates a demand for new books in that category, which encourages authors and publishers to produce new books to meet the demand.
But new folksonomy tags (with important exceptions, as in the realm of activism) are often short-lived and meant to be short-lived, obscure and meant to be obscure. What library cataloguer would think to accommodate the category #glitterhorse, which has a surprising number of posts on Twitter and Instagram? How can Vander Wal’s original definition of folksonomy as a tool for information retrieval accommodate tags that function, not as search terms, but as theatrical asides, like #sorrynotsorry? What about tags that are so narrowly specific that no search could ever turn up more than one usage?
Perhaps the best way to understand the weird things that people do with folksonomy tags is to appeal, not to information science, but to narratology, the study of narrative structures. …(More)”.
Scott Falk, John Cherf and Julie Schulz at the Harvard Business Review: “We recently conducted an in-depth study at Lumere to gain insight into physicians’ perceptions of clinical variation and the factors influencing their choices of drugs and devices. Based on a survey of 276 physicians, our study results show that it’s necessary to consistently and frequently share cost data and clinical evidence with physicians, regardless of whether they’re affiliated with or directly employed by a hospital….
There are multiple explanations as to why health system administrators have been slow to share data with physicians. The two most common challenges are difficulty obtaining accurate, clinically meaningful data and lack of knowledge among administrators about communicating data.
When it comes to obtaining accurate, meaningful data, the reality is that many health systems do not know where to start. Between disparate data-collection systems, varied physician needs, and an overwhelming array of available clinical evidence, it can be daunting to try to develop a robust, yet streamlined, approach.
As for the second problem, many administrators have simply not been trained to effectively communicate data. Health system leaders tend to be more comfortable talking about costs, but physicians generally focus on clinical outcomes. As a result, physicians frequently have follow-up questions that administrators interpret as pushback. It is important to understand what physicians need.
Determine the appropriate amount and type of data to share. Using evidence and data can foster respectful debate, provide honest education, and ultimately align teams.
Physicians are driven by their desire to improve patient outcomes and therefore want the total picture. This includes access to published evidence to help choose cost-effective drug and device alternatives without hurting outcomes. Health system administrators need to provide clinicians with access to a wide range of data (not only data about costs). Ensuring that physicians have a strong voice in determining which data to share will help create alignment and trust. A more nuanced value-based approach that accounts for important clinical and patient-centered outcomes (e.g., length of stay, post-operative recovery profile) combined with cost data may be the most effective solution.
While physicians generally report wanting more cost data, not all physicians have the experience and training to appropriately incorporate it into their decision making. Surveyed physicians who have had exposure to a range of cost data, data highlighting clinical variation, and practice guidelines generally found cost data more influential in their selection of drugs and devices, regardless of whether they shared in savings under value-based care models. This was particularly true for more veteran physicians and those with private-practice experience who have had greater exposure to managing cost information.
Health systems can play a key role in helping physicians use cost and quality data to make cost-effective decisions. We recommend that health systems identify a centralized data/analytics department that includes representatives of both quality-improvement teams and technology/informatics to own the process of streamlining, analyzing, and disseminating data.
Compare data based on contemporary evidence-based guidelines. Physicians would like to incorporate reliable data into their decision-making when selecting drugs and devices. In our survey, 54% of respondents reported that it was either “extremely important” or “very important” that hospitals use peer-reviewed literature and clinical evidence to support the selection of medical devices. Further, 56% of respondents said it was “extremely important” or “very important” that physicians be involved in using data to develop clinical protocols, guidelines, and best practices….(More)”.
Public Knowledge: “Today, we’re happy to announce our newest white paper, “The Inevitability of AI Law & Policy: Preparing Government for the Era of Autonomous Machines,” by Public Knowledge General Counsel Ryan Clough. The paper argues that the rapid and pervasive rise of artificial intelligence risks exploiting the most marginalized and vulnerable in our society. To mitigate these harms, Clough advocates for a new federal authority to help the U.S. government implement fair and equitable AI. Such an authority should provide the rest of the government with the expertise and experience needed to achieve five goals crucial to building ethical AI systems:
- Boosting sector-specific regulators and confronting overarching policy challenges raised by AI;
- Protecting public values in government procurement and implementation of AI;
- Attracting AI practitioners to civil service, and building durable and centralized AI expertise within government;
- Identifying major gaps in the laws and regulatory frameworks that govern AI; and
- Coordinating strategies and priorities for international AI governance.
“Any individual can be misjudged and mistreated by artificial intelligence,” Clough explains, “but the record to date indicates that it is significantly more likely to happen to the less powerful, who also have less recourse to do anything about it.” The paper argues that a new federal authority is the best way to meet the profound and novel challenges AI poses for us all….(More)”.
Book by W. Kip Viscusi: “Like it or not, sometimes we need to put a monetary value on people’s lives. In the past, government agencies used the financial “cost of death” to monetize the mortality risks of regulatory policies, but this method vastly undervalued life. Pricing Lives tells the story of how the government came to adopt an altogether different approach–the value of a statistical life, or VSL—and persuasively shows how its more widespread use could create a safer and more equitable society for everyone.
In the 1980s, W. Kip Viscusi used the method to demonstrate that the benefits of requiring businesses to label hazardous chemicals immensely outweighed the costs. VSL is the risk-reward trade-off that people make about their health when considering risky job choices. With it, Viscusi calculated how much more money workers would demand to take on hazardous jobs, boosting calculated benefits by an order of magnitude. His current estimate of the value of a statistical life is $10 million. In this book, Viscusi provides a comprehensive look at all aspects of economic and policy efforts to price lives, including controversial topics such as whether older people’s lives are worth less and richer people’s lives are worth more. He explains why corporations need to abandon the misguided cost-of-death approach, how the courts can profit from increased application of VSL in assessing liability and setting damages, and how other countries consistently undervalue risks to life.
Pricing Lives proposes sensible economic guideposts to foster more protective policies and greater levels of safety in the United States and throughout the world….(More)”.
Book by In recent years a global network of science has emerged as a result of thousands of individual scientists seeking to collaborate with colleagues around the world, creating a network which rises above national systems. The globalization of science is part of the underlying shift in knowledge creation generally: the collaborative era in science. Over the past decade, the growth in the amount of knowledge and the speed at which it is available has created a fundamental shift—where data, information, and knowledge were once scarce resources, they are now abundantly available.
Collaboration, openness, customer- or problem-focused research and development, altruism, and reciprocity are notable features of abundance, and they create challenges that economists have not yet studied. This book defines the collaborative era, describes how it came to be, reveals its internal dynamics, and demonstrates how real-world practitioners are changing to take advantage of it. Most importantly, the book lays out a guide for policymakers and entrepreneurs as they shift perspectives to take advantage of the collaborative era in order to create social and economic welfare….(More)”.
Paper by Basma Albanna and Richard Heeks: “Positive deviance is a growing approach in international development that identifies those within a population who are outperforming their peers in some way, eg, children in low‐income families who are well nourished when those around them are not. Analysing and then disseminating the behaviours and other factors underpinning positive deviance are demonstrably effective in delivering development results.
However, positive deviance faces a number of challenges that are restricting its diffusion. In this paper, using a systematic literature review, we analyse the current state of positive deviance and the potential for big data to address the challenges facing positive deviance. From this, we evaluate the promise of “big data‐based positive deviance”: This would analyse typical sources of big data in developing countries—mobile phone records, social media, remote sensing data, etc—to identify both positive deviants and the factors underpinning their superior performance.
While big data cannot solve all the challenges facing positive deviance as a development tool, they could reduce time, cost, and effort; identify positive deviants in new or better ways; and enable positive deviance to break out of its current preoccupation with public health into domains such as agriculture, education, and urban planning. In turn, positive deviance could provide a new and systematic basis for extracting real‐world development impacts from big data…(More)”.
Book edited by Allan Afuah, Christopher L. Tucci, and Gianluigi Viscusi: “Examples of the value that can be created and captured through crowdsourcing go back to at least 1714 when the UK used crowdsourcing to solve the Longitude Problem, obtaining a solution that would enable the UK to become the dominant maritime force of its time. Today, Wikipedia uses crowds to provide entries for the world’s largest and free encyclopedia. Partly fueled by the value that can be created and captured through crowdsourcing, interest in researching the phenomenon has been remarkable.
Despite this – or perhaps because of it – research into crowdsourcing has been conducted in different research silos, within the fields of management (from strategy to finance to operations to information systems), biology, communications, computer science, economics, political science, among others. In these silos, crowdsourcing takes names such as broadcast search, innovation tournaments, crowdfunding, community innovation, distributed innovation, collective intelligence, open source, crowdpower, and even open innovation. This book aims to assemble chapters from many of these silos, since the ultimate potential of crowdsourcing research is likely to be attained only by bridging them. Chapters provide a systematic overview of the research on crowdsourcing from different fields based on a more encompassing definition of the concept, its difference for innovation, and its value for both private and public sector….(More)”.
Zeynep Engin and Philip Treleaven in the Computer Journal: “The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the ‘smartification’ of public services and the national infrastructure will be much more significant in comparison to any other sector given government’s function and importance to every institution and individual.
Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major ‘client’ and also ‘public champion’ for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants….(More)”.