New paper by Mathieu O’Neil in the special issue on Contested Internet Governance of the Revue française d’études américaines: “Online projects are communities of practice which attempt to bypass the hierarchies of everyday life and to create autonomous institutions and forms of organisation. A wealth of theoretical frameworks have been put forward to account for these networked actors’ capacity to communicate and self-organise. This article reviews terminology used in Internet research and assesses what it implies for the understanding of regulatory-oriented collective action. In terms of the environment in which interpersonal communication occurs, what differences does it make to speak of “public spheres” or of “public spaces”? In terms of social formations, of “organisations” or “networks”? And in terms of the diffusion of information over the global network, of “contagion” or “trajectories”? Selecting theoretical frames is a momentous decision for researchers, as it authorises or forbids the analysis of different types of behaviour and practices”.-
Other papers on Internet Governance in the Revue:
Divina Frau-Meigs (Ed.). Conducting Research on the Internet and its Governance
The Internet and its Governance: A General Bibliography
Glossary of Key Terms and Notions about Internet Governance
Julia Pohle et Luciano Morganti The Internet Corporation for Assigned Names and Numbers (ICANN): Origins, Stakes and Tensions
Francesca Musiani et al. Net Neutrality as an Internet Governance Issue: The Globalization of an American-Born Debate
Jeanette Hofmann Narratives of Copyright Enforcement: The Upward Ratchet and the Sleeping Giant
Elizabeth Dubois et William H. Dutton The Fifth Estate in Internet Governance: Collective Accountability of a Canadian Policy Initiative
Mathieu O’Neil Collaborative Internet Governance: Terms and Conditions of Analysis
Peng Hwa Ang et Natalie Pang Globalization of the Internet, Sovereignty or Democracy: The Trilemma of the Internet Governance Forum
Data Discrimination Means the Poor May Experience a Different Internet
MIT Technology Review: “Data analytics are being used to implement a subtle form of discrimination, while anonymous data sets can be mined to reveal health data and other private information, a Microsoft researcher warned this morning at MIT Technology Review’s EmTech conference.
Kate Crawford, principal researcher at Microsoft Research, argued that these problems could be addressed with new legal approaches to the use of personal data.
In a new paper, she and a colleague propose a system of “due process” that would give people more legal rights to understand how data analytics are used in determinations made against them, such as denial of health insurance or a job. “It’s the very start of a conversation about how to do this better,” Crawford, who is also a visiting professor at the MIT Center for Civic Media, said in an interview before the event. “People think ‘big data’ avoids the problem of discrimination, because you are dealing with big data sets, but in fact big data is being used for more and more precise forms of discrimination—a form of data redlining.”
During her talk this morning, Crawford added that with big data, “you will never know what those discriminations are, and I think that’s where the concern begins.”
Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco
New paper by JH Lee, MG Hancock, MC Hu in Technological Forecasting and Social Change: “This study aims to shed light on the process of building an effective smart city by integrating various practical perspectives with a consideration of smart city characteristics taken from the literature. We developed a framework for conducting case studies examining how smart cities were being implemented in San Francisco and Seoul Metropolitan City. The study’s empirical results suggest that effective, sustainable smart cities emerge as a result of dynamic processes in which public and private sector actors coordinate their activities and resources on an open innovation platform. The different yet complementary linkages formed by these actors must further be aligned with respect to their developmental stage and embedded cultural and social capabilities. Our findings point to eight ‘stylized facts’, based on both quantitative and qualitative empirical results that underlie the facilitation of an effective smart city. In elaborating these facts, the paper offers useful insights to managers seeking to improve the delivery of smart city developmental projects.”
From Collective Intelligence to Collective Intelligence Systems
New Paper by A. Kornrumpf and U. Baumol in the International Journal of Cooperative Information Systems: “Collective intelligence (CI) has become a popular research topic over the past few years. However, the CI debate suffers from several problems such as that there is no unanimously agreed-upon definition of CI that clearly differentiates between CI and related terms such as swarm intelligence (SI) and collective intelligence systems (CIS). Furthermore, a model of such CIS is lacking for purposes of research and the design of new CIS. This paper aims at untangling the definitions of CI and other related terms, especially CIS, and at providing a semi-structured model of CIS as a first step towards more structured research. The authors of this paper argue that CI can be defined as the ability of sufficiently large groups of individuals to create an emergent solution for a specific class of problems or tasks. The authors show that other alleged properties of CI which are not covered by this definition, are, in fact, properties of CIS and can be understood by regarding CIS as complex socio-technical systems (STS) that enable the realization of CI. The model defined in this article serves as a means to structure open questions in CIS research and helps to understand which research methodology is adequate for different aspects of CIS.”
Towards an information systems perspective and research agenda on crowdsourcing for innovation
New paper by A Majchrzak and A Malhotra in The Journal of Strategic Information Systems: “Recent years have seen an increasing emphasis on open innovation by firms to keep pace with the growing intricacy of products and services and the ever changing needs of the markets. Much has been written about open innovation and its manifestation in the form of crowdsourcing. Unfortunately, most management research has taken the information system (IS) as a given. In this essay we contend that IS is not just an enabler but rather can be a shaper that optimizes open innovation in general and crowdsourcing in particular. This essay is intended to frame crowdsourcing for innovation in a manner that makes more apparent the issues that require research from an IS perspective. In doing so, we delineate the contributions that the IS field can make to the field of crowdsourcing.
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Reviews participation architectures supporting current crowdsourcing, finding them inadequate for innovation development by the crowd.
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Identifies 3 tensions for explaining why a participation architecture for crowdsourced innovation is difficult.
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Identifies affordances for the participation architectures that may help to manage the tension.
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Uses the tensions and possible affordances to identify research questions for IS scholars.”
Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo
New paper by Michael J. Madison: “The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, some times known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.”
Are Some Tweets More Interesting Than Others? #HardQuestion
New paper by Microsoft Research (Omar Alonso, Catherine C. Marshall, and Marc Najork): “Twitter has evolved into a significant communication nexus, coupling personal and highly contextual utterances with local news, memes, celebrity gossip, headlines, and other microblogging subgenres. If we take Twitter as a large and varied dynamic collection, how can we predict which tweets will be interesting to a broad audience in advance of lagging social indicators of interest such as retweets? The telegraphic form of tweets, coupled with the subjective notion of interestingness, makes it difficult for human judges to agree on which tweets are indeed interesting.
In this paper, we address two questions: Can we develop a reliable strategy that results in high-quality labels for a collection of tweets, and can we use this labeled collection to predict a tweet’s interestingness?
To answer the first question, we performed a series of studies using crowdsourcing to reach a diverse set of workers who served as a proxy for an audience with variable interests and perspectives. This method allowed us to explore different labeling strategies, including varying the judges, the labels they applied, the datasets, and other aspects of the task.
To address the second question, we used crowdsourcing to assemble a set of tweets rated as interesting or not; we scored these tweets using textual and contextual features; and we used these scores as inputs to a binary classifier. We were able to achieve moderate agreement (kappa = 0.52) between the best classifier and the human assessments, a figure which reflects the challenges of the judgment task.”
Defining Open Data
Open Knowledge Foundation Blog: “Open data is data that can be freely used, shared and built-on by anyone, anywhere, for any purpose. This is the summary of the full Open Definition which the Open Knowledge Foundation created in 2005 to provide both a succinct explanation and a detailed definition of open data.
As the open data movement grows, and even more governments and organisations sign up to open data, it becomes ever more important that there is a clear and agreed definition for what “open data” means if we are to realise the full benefits of openness, and avoid the risks of creating incompatibility between projects and splintering the community.
Open can apply to information from any source and about any topic. Anyone can release their data under an open licence for free use by and benefit to the public. Although we may think mostly about government and public sector bodies releasing public information such as budgets or maps, or researchers sharing their results data and publications, any organisation can open information (corporations, universities, NGOs, startups, charities, community groups and individuals).
Read more about different kinds of data in our one page introduction to open data
There is open information in transport, science, products, education, sustainability, maps, legislation, libraries, economics, culture, development, business, design, finance …. So the explanation of what open means applies to all of these information sources and types. Open may also apply both to data – big data and small data – or to content, like images, text and music!
So here we set out clearly what open means, and why this agreed definition is vital for us to collaborate, share and scale as open data and open content grow and reach new communities.
As the open data movement grows, and even more governments and organisations sign up to open data, it becomes ever more important that there is a clear and agreed definition for what “open data” means if we are to realise the full benefits of openness, and avoid the risks of creating incompatibility between projects and splintering the community.
Open can apply to information from any source and about any topic. Anyone can release their data under an open licence for free use by and benefit to the public. Although we may think mostly about government and public sector bodies releasing public information such as budgets or maps, or researchers sharing their results data and publications, any organisation can open information (corporations, universities, NGOs, startups, charities, community groups and individuals).
Read more about different kinds of data in our one page introduction to open data
There is open information in transport, science, products, education, sustainability, maps, legislation, libraries, economics, culture, development, business, design, finance …. So the explanation of what open means applies to all of these information sources and types. Open may also apply both to data – big data and small data – or to content, like images, text and music!
So here we set out clearly what open means, and why this agreed definition is vital for us to collaborate, share and scale as open data and open content grow and reach new communities.
What is Open?
The full Open Definition provides a precise definition of what open data is. There are 2 important elements to openness:
- Legal openness: you must be allowed to get the data legally, to build on it, and to share it. Legal openness is usually provided by applying an appropriate (open) license which allows for free access to and reuse of the data, or by placing data into the public domain.
- Technical openness: there should be no technical barriers to using that data. For example, providing data as printouts on paper (or as tables in PDF documents) makes the information extremely difficult to work with. So the Open Definition has various requirements for “technical openness,” such as requiring that data be machine readable and available in bulk.”…
The role of task difficulty in the effectiveness of collective intelligence
New article by Christian Wagner: “The article presents a framework and empirical investigation to demonstrate the role of task difficulty in the effectiveness of collective intelligence. The research contends that collective intelligence, a form of community engagement to address problem solving tasks, can be superior to individual judgment and choice, but only when the addressed tasks are in a range of appropriate difficulty, which we label the “collective range”. Outside of that difficulty range, collectives will perform about as poorly as individuals for high difficulty tasks, or only marginally better than individuals for low difficulty tasks. An empirical investigation with subjects randomly recruited online supports our conjecture. Our findings qualify prior research on the strength of collective intelligence in general and offer preliminary insights into the mechanisms that enable individuals and collectives to arrive at good solutions. Within the framework of digital ecosystems, the paper argues that collective intelligence has more survival strength than individual intelligence, with highest sustainability for tasks of medium difficulty”
A New Kind of Economy is Born – Social Decision-Makers Beat the "Homo Economicus"
A new paper by Dirk Helbing: “The Internet and Social Media change our way of decision-making. We are no longer the independent decision makers we used to be. Instead, we have become networked minds, social decision-makers, more than ever before. This has several fundamental implications. First of all, our economic theories must change, and second, our economic institutions must be adapted to support the social decision-maker, the “homo socialis”, rather than tailored to the perfect egoist, known as “homo economicus”….
Such developments will eventually create a participatory market society. “Prosumers”, i.e. co-producing consumers, the new “makers” movement, and the sharing economy are some examples illustrating this. Just think of the success of Wikipedia, Open Streetmap or Github. Open Streetmap now provides the most up-to-date maps of the world, thanks to more than 1 million volunteers.
This is just the beginning of a new era, where production and public engagement will more and more happen in a bottom up way through fluid “projects”, where people can contribute as a leaders (“entrepreneurs”) or participants. A new intellectual framework is emerging, and a creative and participatory era is ahead.
The paradigm shift towards participatory bottom-up self-regulation may be bigger than the paradigm shift from a geocentric to a heliocentric worldview. If we build the right institutions for the information society of the 21st century, we will finally be able to mitigate some very old problems of humanity. “Tragedies of the commons” are just one of them. After so many centuries, they are still plaguing us, but this needn’t be.”