Le public acquiert ainsi une nouvelle fonction : celle d’instance critique auquel doit s’exposer le pouvoir.

“NextNow Collaboratory is an interesting example of a new kind of collective intelligence: an Internet-enabled, portable social network, easily transferable from one social cause to another.”

Four databases of citizen science and crowdsourcing projects —  SciStarter, the Citizen Science Association (CSA), CitSci.org, and the Woodrow Wilson International Center for Scholars (the Wilson Center Commons Lab) — are working on a common project metadata schema to support data sharing with the goal of maintaining accurate and up to date information about citizen science projects.  The federal government is joining this conversation with a cross-agency effort to promote citizen science and crowdsourcing as a tool to advance agency missions. Specifically, the White House Office of Science and Technology Policy (OSTP), in collaboration with the U.S. Federal Community of Practice for Citizen Science and Crowdsourcing (FCPCCS),is compiling an Open Innovation Toolkit containing resources for federal employees hoping to implement citizen science and crowdsourcing projects. Navigation through this toolkit will be facilitated in part through a system of metadata tags. In addition, the Open Innovation Toolkit will link to the Wilson Center’s database of federal citizen science and crowdsourcing projects.These groups became aware of their complementary efforts and the shared challenge of developing project metadata tags, which gave rise to the need of a workshop.  

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In amplifying our intelligence, we are applying the principle of synergistic structuring that was followed by natural evolution in developing the basic human capabilities. What we have done in the development of our augmentation means is to construct a superstructure that is a synthetic extension of the natural structure upon which it is built. In a very real sense, as represented by the steady evolution of our augmentation means, the development of "artificial intelligence" has been going on for centuries.

Our culture has evolved means for us to organize the little things we can do with our basic capabilities so that we can derive comprehension from truly complex situations, and accomplish the processes of deriving and implementing problem solutions. The ways in which human capabilities are thus extended are here called augmentation means, and we define four basic classes of them: 2a4 Artifacts—physical objects designed to provide for human comfort, for the manipulation of things or materials, and for the manipulation of symbols.2a4a Language—the way in which the individual parcels out the picture of his world into the concepts that his mind uses to model that world, and the symbols that he attaches to those concepts and uses in consciously manipulating the concepts ("thinking"). 2a4b Methodology—the methods, procedures, strategies, etc., with which an individual organizes his goal-centered (problem-solving) activity. 2a4c Training—the conditioning needed by the human being to bring his skills in using Means 1, 2, and 3 to the point where they are operationally effective. 2a4d The system we want to improve can thus be visualized as a trained human being together with his artifacts, language, and methodology. The explicit new system we contemplate will involve as artifacts computers, and computer-controlled information-storage, information-handling, and information-display devices. The aspects of the conceptual framework that are discussed here are primarily those relating to the human being's ability to make significant use of such equipment in an integrated system.

multiple and collective

It should be no surprise recognizing the problematic-theoretical simi­larities between disaster research and its "half-sibling" (e.g., see Wenger 1987), collective behavior. Both disaster research and collective behavior are at a theoretical standstill (e.g., see Aguirre and Quarantelli 1983), and both still rely upon every-day language rather than a broader scheme for describing events (e.g., see Weller and Quarantelli 1973, McPhail 1992). Works by Kreps and Bosworth (1994) and Dombrowsky (1981; 1987), (and perhaps Barton's 1970 classic work) reflect the theoretical attention disaster research needs to build.

Also, with disaster research having strong theoretical ties with the study of collective behavior(Wenger 1987), and with the field of collective behavior often looking at issues related to social change {e.g., riots, social move­ments), another link between disasters and social change has implicitly

By examiningwork practices, and tracing how those practices are reified in the social-technical organization of a group that is forming and stabilizing as they do the work, we learn not just what this particular group did, but also how the mechanisms by which collective action in digital environments are organizedbottom-up. We also learn how those lessonsaregraduated into prescriptivetop-down direction to sustain and direct future action

Perpetual progress is a strong statement of the transhumanist commitment to seek “more intelligence, wisdom, and effectiveness, an open-ended lifespan, and the removal of political, cultural, biological, and psychological limits to continuing development. Perpetually overcoming constraints on our progress and possibilities as individuals, as organizations, and as a species. Growing in healthy directions without bound.”

Capacity can also affect crisis potential through staffing decisions that affect the diversity of acts that are available. Enactment is labour-intensive, which means understaffing has serious effects.

"... groups of individuals doing things collectively that seem intelligent.” [41]

hus it becomes possible to see how ques-tions around data use need to shift from asking what is in the data, to include discussions of how the data is structured, and how this structure codifies value systems and social practices, subject positions and forms of visibility and invisi-bility (and thus forms of surveillance), along with the very ideas of crisis, risk governance and preparedness. Practices around big data produce and perpetuate specific forms of social engagement as well as understandings of the areas affected and the people being served.

Big data is not just about knowing more. It could be – and should be – about knowing better or about changing what knowing means. It is an ethico- episteme-ontological- political matter. The ‘needle in the haystack’ metaphor conceals the fact that there is no such thing as one reality that can be revealed. But multiple, lived are made through mediations and human and technological assemblages. Refugees’ realities of intersecting intelligences are shaped by the ethico- episteme-ontological politics of big data.

Burns (2015) builds on this to investigate how within digital humanitarianism discourses, big data produce and perform subjects ‘in need’ (individuals or com-munities affected by crises) and a humanitarian ‘saviour’ community that, in turn, seeks answers through big data

Crises are often not a crisis of information. It is often not a lack of data or capacity to analyse it that prevents ‘us’ from pre-venting disasters or responding effectively. Risk management fails because there is a lack of a relational sense of responsibility. But this does not have to be the case. Technologies that are designed to support collaboration, such as what Jasanoff (2007) terms ‘technologies of humility’, can be better explored to find ways of framing data and correlations that elicit a greater sense of relational responsibility and commitment.

In some ways this constitutes the production of ‘liquid resilience’ – a deflection of risk to the individuals and communities affected which moves us from the idea of an all-powerful and knowing state to that of a ‘plethora of partial projects and initiatives that are seeking to harness ICTs in the service of better knowing and governing individuals and populations’ (Ruppert 2012: 118)

Indeed, as Chandler (2015: 9) also argues, crowdsourcing of big data does not equate to a democratisation of risk assessment or risk governance:

ut Burns finds that humanitarian staff often describe the local communities and ‘crowds’ as the ‘eyes, ears and sensors’ of UN staff, which does not index a genuine collaborative relationship. He states: ‘In all these cases, the discourse talks of putting local people “in the driving seat” when in reality the direction of the journey has already been decided’ (Burns 2015: 48). Burns (2015: 42) also notes that this leads to a transformation of social responsibility into individual responsibility.Neoliberalism’s promotion of free market norms is therefore much more than the simple ideology of free market economics. It is a specific form of social rule that institutionalises a rationality of competition, enterprise indi-vidualised responsibility. Although the state ‘steps back’ and encourages the free conduct of individuals, this is achieved through active intervention into civil society and the opening up of new areas to the logic of private enter-prise and individual initiative. This is the logic behind the rise of resilience

The UNHCR has even called for the refugees themselves to also develop their own data solutions and ideas (see Palmer 2014) as a way to help build their ideologies into the data infrastructures and thus bring their prisms into view. This could create a richer situational awareness and a better ability to understand and deal with unfolding and future crises by supporting resilient communities through giving them the means of data producing and sharing

Doing so switches the discourse from vulnerability, where there is a need for external protection mobilised from above to come in and rescue the refugees, to one of resilience, where self- sufficiency and autonomy are part of the equation (Meier 2013).

Yet, at the same time as power is exercised by both the state and corporations, power is gathering from the bottom up in new ways. In disaster response, a dynamic interplay between publics and experts is captured by the concept of social collective intelligence (Büscher et al. 2014); a disruptive innovative force that is challenging the social, economic, political and organisational practices that shape disaster response.

Since the data is already being collected on a regular basis by ubiquitous private firms, it is thought to contain information that will increase opportunities for intelligence gathering and thereby security. This marks a shift from surveillance to ‘dataveillance’ (van Dijck 2014), where the impetus for data processing is no longer motivated by specific purposes or suspicions, but opportunistic discovery of anomalies that can be investigated. For crisis management this could mean benefits such as richer situation awareness, increased capacity for risk assess-ment, anticipation and prediction, as well as more agile response

lthough Snowden’s revelations shocked the world and prompted calls for a public debate on issues of privacy and transparency

While seemingly avoiding the traps of exerting top- down power over people the state does not yet have formal control over, and simultaneously providing support for self- determination and choice to empower individuals for self- sufficiency rather than defining them as vulnerable and passive recipients of top- down protection (Meier 2013), tying individual aid to mobile tracking puts refugees in a situation where their security is dependent upon individual choice and the private sector. Apart from disrupting traditional dynamics of responsibility for aid and protection, public–private sharing of intel-ligence brings new forms of dataveillance

However, with these big data collections, the focus becomes not the individu-al’s behaviour but social and economic insecurities, vulnerabilities and resilience in relation to the movement of such people. The shift acknowledges that what is surveilled is more complex than an individual person’s movements, communica-tions and actions over time.

Andrejevic and Gates (2014: 190) suggest that ‘the target becomes the hidden patterns in the data, rather than particular individuals or events’. National and local authorities are not seeking to monitor individuals and discipline their behaviour but to see how many people will reach the country and when, so that they can accommodate them, secure borders, and identify long- term social out-looks such as education, civil services, and impacts upon the host community (Pham et al. 2015).

as boyd and Crawford argue, ‘without taking into account the sample of a data set, the size of the data set is meaningless’ (2012: 669). Furthermore, many tech-niques used by the state and corporations in big data analysis are based on probabilistic prediction which, some experts argue, is alien to, and even incom-prehensible for, human reasoning (Heaven 2013). As Mayer-Schönberger stresses, we should be ‘less worried about privacy and more worried about the abuse of probabilistic prediction’ as these processes confront us with ‘profound ethical dilemmas’ (in Heaven 2013: 35).

Second, this tracking and tracing of refugees has become a deeply ambiguous process in a world riven by political conflict, where ‘migration’ increasingly comes to be discussed in co- location with terrorism.

Surveillance studies have tracked a shift from discipline to control (Deleuze 1992; Haggerty and Ericson 2000; Lyon 2014) exemplified by the shift from monitoring confined populations (through technologies such as the panopticon) to using new technologies to keep track of mobile populations.

As Coyle and Meier (2009) argue, disasters are often seen as crises of information where it is vital to make sure that people know where to find potable water, how to ask for help, where their relatives are, or if their home is at risk; as well as providing emergency response and human-itarian agencies with information about affected populations. Such a quest for information for ‘security’, in turn, provides fertile ground for a quest for technological solutions, such as big data, which open up opportunities for the extended surveillance of everyday life. The assumption is that if only enough information could be gathered and exchanged, preparedness, resilience and control would follow. This is particularly pertinent with regard to mobile pop-ulations (Adey and Kirby 2016)

hird, at this juncture, control is being equated with visibility and visibility with personal security. But how these individuals are made visible matters for both privacy and security, let alone the politics of conflating refugees, migration and terrorism. Indeed, working with specific data framing mechanisms affects how the causes and effects of disasters are identified and what elements and people are considered (Frickel 2008

First, there is a double dynamic to the generation of data in the refugee crisis.

Datafication refers to the fact that ‘we can now capture and calculate at a much more comprehensive scale the physical and intangible aspects of existence and act on them’ (Mayer- Schönberger and Cukier 2013: 97

There is an uneasy coming together of diverse computational and human intelligences in these intersections, and the ambiguous nature of intelligence – understood, on the one hand, as a capacity for perceiving, learning and under-standing and, on the other, as information obtained for strategic purposes – marks complex relationships between ‘good’ and ‘dark’ aspects of big data, surveil-lance and crisis management.

Peer production successfully elicits contributions from diverse individu-als with diverse motivations – a quality that continues to distinguish it fromsimilar forms of collective intelligence

Although peer production is central to social scientific and legal researchon collective intelligence, not all examples of collective intelligence created inonline systems are peer production. First, (1) collective intelligence can in-volve centralized control over goal-setting and execution of tasks.

Consistent with this exam-ple, foundational social scientific research relevant to understanding collec-tive intelligence has focused on three central concerns: (1) explaining the or-ganization and governance of decentralized projects, (2) understanding themotivation of contributors in the absence of financial incentives or coerciveobligations, and (3) evaluating the quality of the products generated throughcollective intelligence systems.

Historically,researchers in diverse fields such as communication, sociology, law, and eco-nomics have argued that effective human systems organize people through acombination of hierarchical structures (e.g., bureaucracies), completely dis-tributed coordination mechanisms (e.g., markets), and social institutions ofvarious kinds (e.g., cultural norms). However, the rise of networked systemsand online platforms for collective intelligence has upended many of the as-sumptions and findings from this earlier research.

This point ties into the conceptualisation of time as collec-tive [29] and entangled [43]. The infrastructure that sup-ports a 24/7 society is one that relies on people as well as technologies, the conventional nine-to-five work rhythm, for example, being underpinned by people working shifts outside of these hours.

How can we design for time as collective and interdependent, rather than individualised on the one hand, or explicitly scheduled on the other? What does it mean to position collective time not as something that is achieved when people come together, but as a set of relationships through which they are connected? Both Sharma and Mazmanian and Erickson raise this challenge while highlighting the difficulty in addressing it; neither offer a solution.

"When tasks require high coordination because the work is highly interdependent, having more contributors can increase process losses, reducing the effectiveness of the group below what individual members could optimally accomplish". Having a team too large the overall effectiveness may suffer even when the extra contributors increase the resources. In the end the overall costs from coordination might overwhelm other costs.

Games such as The Sims Series, and Second Life are designed to be non-linear and to depend on collective intelligence for expansion. This way of sharing is gradually evolving and influencing the mindset of the current and future generations.[117] For them, collective intelligence has become a norm.

Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business:[38] Talent utilization At the rate technology is changing, no firm can fully keep up in the innovations needed to compete. Instead, smart firms are drawing on the power of mass collaboration to involve participation of the people they could not employ. This also helps generate continual interest in the firm in the form of those drawn to new idea creation as well as investment opportunities.[38] Demand creation Firms can create a new market for complementary goods by engaging in open source community. Firms also are able to expand into new fields that they previously would not have been able to without the addition of resources and collaboration from the community. This creates, as mentioned before, a new market for complementary goods for the products in said new fields.[38] Costs reduction Mass collaboration can help to reduce costs dramatically. Firms can release a specific software or product to be evaluated or debugged by online communities. The results will be more personal, robust and error-free products created in a short amount of time and costs. New ideas can also be generated and explored by collaboration of online communities creating opportunities for free R&D outside the confines of the company.[38]

To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as "human swarms" modeled after synchronous swarms in nature.

While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group. In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%

To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time

The idea of collective intelligence also forms the framework for contemporary democratic theories often referred to as epistemic democracy.

The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities."

Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.

Thus the grass my horse has bit; the turfs my servant has cut; and the ore I have digged in any place, where I have a right to them in common with others, become my property, without the assignation or consent of any body. The labour that was mine, removing them out of that common state they were in, hath fixed my property in them.

The Semantic Sphere 1

a collective, a community of similarly minded people who helped Sam learn and meet the very particular set of needs that he had

L’expérience littéraire du Général Instin n’est sans doute pas unique. Instin n’invente évidemment pas l’écriture collective, le déplacement des formes et leurs interrelations. En revanche, la durée dans laquelle s’inscrit le projet lui donne une épaisseur et une dimension rare sinon inédite.

What Drives Successful Crowdsourcing?

Microsoft Confirms Its Chinese-Language Chatbot Filters Certain Topics

If "social memory" can be defined as "how and what social groups remember," then digital culture, as Richard Rinehart and Jon Ippolito point out, changes both the how and the what of social memory.

We are each making interventions around education that stress collective rather than individualized, personalized, or privatized modes of learning. For me, a participatory learning environment is one that respects and values the contributions of each participant, whether teacher, student, or someone from the outside community.