in fact the best ideas happen when you are not planning them when you are just creating an environment where people get together in an informal way this is the reason why um Steve Jobs when he designed the Pixar building um he the initial idea was there's just one bathroom for the whole company

Unemployed workers are much more likelyto fall into poverty in countries like the United States, Canada, and Japan,compared with countries such as the Netherlands and Iceland.

The Life We Live and the Life We Experience: Introducing theEpistemological Difference between “Lifeworld” (Lebenswelt) and “LifeConditions” (Lebenslage)

For this reason, the Secretary of State set out a vision1 for health and care to have nationalopen standards for data and interoperability that are mandated throughout the NHS andsocial care.

Building upon Sweeney and Rhinesmith’s approach, and bringing the conceptualizations of care [14,33,34], I propose the following framework:I define social practices as the acts of care performed by individuals and afforded by CTCs in order to promote self and community needs;Based on this study’s ethnography, I categorize social practices into three groups:Care work: the invisible work performed by the infomediaries, or any CTC worker, as described by Sweeney and Rhinesmith;Peer-to-peer care: individuals (CTC users) collaborating with each other so they can inform, take decisions, and strive towards their individual needs; andCommunity care: individuals (CTC users and infomediaries) acting collaboratively or individually in order to promote community wellbeing.It is important to emphasize that social practices also include other social acts that are not necessarily “care”, but given the interactions observed in the CTCs in the favelas, I chose an explicit care-focused lens as the basis of this framework in order to breakdown the social practices in a way that could help make a case for the importance of the CTCs beyond their ICT-focused roles.

Action research also distinguishes itself from other research in that its purpose is to create change on an individual and community level. Kristin Esterberg puts it quite eloquently when she says, “At heart, all action researchers are concerned that research not simply contribute to knowledge but also lead to positive changes in people’s lives” (2002, p. 137).

Almost every social network of note had an early signature proof of work hurdle. For Facebook it was posting some witty text-based status update. For Instagram, it was posting an interesting square photo. For Vine, an entertaining 6-second video. For Twitter, it was writing an amusing bit of text of 140 characters or fewer. Pinterest? Pinning a compelling photo. You can likely derive the proof of work for other networks like Quora and Reddit and Twitch and so on. Successful social networks don't pose trick questions at the start, it’s usually clear what they want from you.

The urgency of time may make it too onerous forthe extra effort of articulating actions as they are beingperformed, yet most emergency response requires somecommunication.

Explicitly articulated narratives mayalso make clearer that multiple sequences of actions maybe occurring simultaneously, thus resolving role conflictsby allowing multiple ways to accomplish a task

Experimentation, the third affordance, refers to theuse of technology to encourage participants to try outnovel ideas.

Recombinability refers to forms of technology-enabled action where individual contributors build oneach others’ contributions.

Reviewability refers to the enactment of technology-enabled new forms of working in which participantsare better able to view and manage the content offront and back narratives over time (West and Lakhani2008). By allowing participants to easily and collab-oratively review a range of ideas, technology-affordedreviewability helps the community respond to tensionsin disembodied ideas, because the reviews can provideimportant contextual information for building on others’ideas.

Technology platforms used by OCs can providea number of affordances for knowledge collabora-tion, three of which we mention here: reviewability,recombinability, and experimentation. These affordancesevolve as new participants provide new ways to use thetechnologies, new social norms are developed around thetechnology affordances, and new needs for fresh affor-dances are identified.

Given the fluid nature of OCsand their rapidly evolving technology platforms, and inline with calls to avoid dualistic thinking about tech-nology (Leonardi and Barley 2008, Markus and Silver2008, Orlikowski and Scott 2008), we suggest technol-ogy affordance as a generative response, one that viewstechnology, action, and roles as emergent, inseparable,and coevolving. Technology affordances offer a relationalperspective on human action, where neither the technol-ogy nor the actor is dominant in the sense that the tech-nology does not define what is possible for the actor todo, nor is the actor free from the limitations of the tech-nological environment. Instead, possibilities for actionemerge from the reciprocal interaction between actor andartifact (Gibson 1979, Zammuto et al. 2007). Thus, anaffordance perspective focuses on the organizing actionsthat are afforded by technology artifacts.

third response to manage tensions is to promoteknowledge collaboration by enacting dynamic bound-aries. In social sciences, although boundaries divide anddisintegrate collectives, they also coordinate and inte-grate social action (Bowker and Star 1999, Lamont andMolnár 2002). Fluidity brings the need for flexible andpermeable boundaries, but it is not only the propertiesof the boundaries but also their dynamicity that helpmanage tensions.

We have observed in OCs that no single narrative isable to keep participants informed about the current stateof the OC with respect to each tension. These commu-nities seem to develop two different types of narratives.Borrowing from Goffman (1959), we label the two nar-ratives the “front” and the “back” narratives.

Based on our collective research on to date, we haveidentified that as tensions ebb and flow, OCs use (or,more precisely, participants engage in) any of the fourtypes of responses that seem to help the OC be gen-erative. The first generative response is labeledEngen-dering Roles in the Moment. In this response, membersenact specific roles that help turn the potentially negativeconsequences of a tension into positive consequences.The second generative response is labeledChannelingParticipation. In this response, members create a nar-rative that helps keep fluid participants informed ofthe state of the knowledge, with this narrative havinga necessary duality between a front narrative for gen-eral public consumption and a back narrative to airthe differences and emotions created by the tensions.The third generative response is labeledDynamicallyChanging Boundaries. In this response, OCs changetheir boundaries in ways that discourage or encouragecertain resources into and out of the communities at cer-tain times, depending on the nature of the tension. Thefourth generative response is labeledEvolving Technol-ogy Affordances. In this response, OCs iteratively evolvetheir technologies in use in ways that are embedded by,and become embedded into, iteratively enhanced socialnorms. These iterations help the OC to socially and tech-nically automate responses to tensions so that the com-munity does not unravel.

Tension 5: Positive and Negative Consequences ofTemporary ConvergenceThe classic models of knowledge collaboration in groupsgive particular weight to the need for convergence. Con-vergence around a single goal, direction, criterion, pro-cess, or solution helps counterbalance the forces ofdivergence, allowing diverse ideas to be framed, ana-lyzed, and coalesced into a single solution (Couger 1996,Isaksen and Treffinger 1985, Osborn 1953, Woodmanet al. 1993). In fluid OCs, convergence is still likelyto exist during knowledge collaboration, but the conver-gence is likely to be temporary and incomplete, oftenimplicit, and is situated among subsets of actors in thecommunity rather than the entire community.

ension 2: Positive and Negative Consequencesof TimeA second tension is between the positive and negativeconsequences of the time that people spend contribut-ing to the OC. Knowledge collaboration requires thatindividuals spend time contributing to the OC’s virtualworkspace (Fleming and Waguespack 2007, Lakhani andvon Hippel 2003, Rafaeli and Ariel 2008). Time has apositive consequence for knowledge collaboration. Themore time people spend evolving others’ contributedideas and responding to others’ comments on thoseideas, the more the ideas can evolve

We argue that it is the fluidity, the tensions that flu-idity creates, and the dynamics in how the OC respondsto these tensions that make knowledge collaboration inOCs fundamentally different from knowledge collabora-tion in teams or other traditional organization structures.

As fluctuations in resource endowments arise overtime because of the fluidity in the OC, these fluctua-tions in resources create fluctuations in tensions, makingsimple structural tactics for managing tensions such ascross-functional teams or divergent opinions (Sheremata2000) inadequate for fostering knowledge collaboration.As complex as these tension fluctuations are for the com-munity, it is precisely these tensions that provide thecatalyst for knowledge collaboration. Communities thatthen respond to these tensions generatively (rather thanin restrictive ways) will be able to realize this potential.Thus, it is not the simple presence of resources that fos-ter knowledge collaboration, but rather the presence ofongoing dynamic tensions within the OC that spur thecollaboration. We describe these tensions in the follow-ing section

Fluidity requires us to look at the dynamics—i.e., thecontinuous and rapid changes in resources—rather thanthe presence or the structural form of the resources.Resources may flow from outside the OC (e.g., pas-sion) or be internally generated (e.g., convergence), sub-sequently influencing and influenced by action (Feldman2004). Resources come with the baggage of having bothpositive and negative consequences for knowledge col-laboration, creating a tension within the community inhow to manage the positive and negative consequencesin a manner similar to the one faced by ambidextrousorganizations (O’Reilly and Tushman 2004).

However, failure to examine the critical roleof even the inactive participants in the functioning of thecommunity is to ignore that passive (and invisible) par-ticipation may be a step toward greater participation, aswhen individuals use passivity as a way to learn aboutthe collective in a form of peripheral legitimate partici-pation (Lave and Wenger 1991, Yeow et al. 2006).

Fluidity recognizes the highly flexible or permeableboundaries of OCs, where it is hard to figure out whois in the community and who is outside (Preece et al.2004) at any point in time, let alone over time. Theyare adaptive in that they change as the attention, actions,and interests of the collective of participants change overtime. Many individuals in an OC are at various stagesof exit and entry that change fluidly over time.

We argue that fluid-ity is a fundamental characteristic of OCs that makesknowledge collaboration in such settings possible. Assimply depicted in Figure 1, we envision OCs as fluidorganizational objects that are simultaneously morphingand yet retaining a recognizable shape (de Laet and Mol2000, Law 2002, Mol and Law 1994).

a growing consensus on factors that moti-vate people to make contributions to these communities,including motivational factors based on self-interest (e.g.,Lakhani and von Hippel 2003, Lerner and Tirole 2002,von Hippel and von Krogh 2003), identity (Bagozzi andDholakia 2006, Blanchard and Markus 2004, Ma andAgarwal 2007, Ren et al. 2007, Stewart and Gosain2006), social capital (Nambisan and Baron 2010; Waskoand Faraj 2000, 2005; Wasko et al. 2009), and socialexchange (Faraj and Johnson 2011).

For instance, knowledge collaboration in OCscan occur without the structural mechanisms tradition-ally associated with knowledge collaboration in orga-nizational teams: stable membership, convergence afterdivergence, repeated people-to-people interactions, goal-sharing, and feelings of interdependence among groupmembers (Boland et al. 1994, Carlile 2002, Dougherty1992, Schrage 1995, Tsoukas 2009).

Knowledge collaboration is defined broadly as thesharing, transfer, accumulation, transformation, andcocreation of knowledge. In an OC, knowledge collab-oration involves individual acts of offering knowledgeto others as well as adding to, recombining, modify-ing, and integrating knowledge that others have con-tributed. Knowledge collaboration is a critical elementof the sustainability of OCs as individuals share andcombine their knowledge in ways that benefit them per-sonally, while contributing to the community’s greaterworth (Blanchard and Markus 2004, Jeppesen andFredericksen 2006, Murray and O’Mahoney 2007, vonHippel and von Krogh 2006, Wasko and Faraj 2000).

Online communities (OCs) are open collectives of dis-persed individuals with members who are not necessarilyknown or identifiable and who share common inter-ests, and these communities attend to both their indi-vidual and their collective welfare (Sproull and Arriaga2007).

The situated and emergent nature of coordinationdoes not imply that practices are completely uniqueand novel. On the one hand, they vary accordingto the logic of the situation and the actors present.On the other hand, as seen in our categorizationof dialogic coordination, they follow a recognizablelogic and are only partially improvised. This tensionbetween familiarity and uniqueness of response is atthe core of a practice view of work (Orlikowski 2002).

Recently, Brown and Duguid (2001, p. 208) sug-gested that coordination of organizational knowledgeis likely to be more challenging than coordination ofroutine work, principally because the “elements to becoordinated are not just individuals but communitiesand the practices they foster.” As we found in ourinvestigation of coordination at the boundary, signif-icant epistemic differences exist and must be recog-nized. As the dialogic practices enacted in responseto problematic trajectories show, the epistemic dif-ferences reflect different perspectives or prioritiesand cannot be bridged through better knowledge

nature point to the limitations of a structuralist viewof coordination. In the same way that an organi-zational routine may unfold differently each timebecause it cannot be fully specified (Feldman andPentland 2003), coordination will vary each time.Independent of embraced rules and programs, therewill always be an element of bricolage reflecting thenecessity of patching together working solutions withthe knowledge and resources at hand (Weick 1993).Actors and the generative schemes that propel theiractions under pressure make up an important com-ponent of coordination’s modus operandi (Bourdieu1990, Emirbayer and Mische 1998).

These practices are highly situated, emer-gent, and contextualized and thus cannot be prespec-ified the way traditional coordination mechanismscan be. Thus, recent efforts based on an information-processing view to develop typologies of coordina-tion mechanisms (e.g., Malone et al. 1999) may be tooformal to allow organizations to mount an effectiveresponse to events characterized by urgency, novelty,surprise, and different interpretations.

Our findings also point to a broader divide in coor-dination research. Much of the power of traditionalcoordination models resides in their information-processing basis and their focus on the design issuessurrounding work unit differentiation and integra-tion. This design-centric view with its emphasis onrules,structures,andmodalitiesofcoordinationislessuseful for studying knowledge work.

Boundarywork requires the ability to see perspectives devel-oped by people immersed in a different commu-nity of knowing (Boland and Tenkasi 1995, Star andGriesemer 1989). Often, particular disciplinary focilead to differences in opinion regarding what stepsto take next in treating the patient.

The termdialogic—as opposed to monologic—recognizes dif-ferences and emphasizes the existence of epistemicboundaries, different understandings of events, andthe existence of boundary objects (e.g., the diagnosisor the treatment plan). A dialogic approach to coordi-nation is the recognition that action, communication,and cognition are essentially relational and highlysituated. We use the concept of trajectory (Bourdieu1990, Strauss 1993) to recognize that treatment pro-gressions are not always linear or positive.

A dialogic coordination practice differs from moregeneral expertise coordination processes in that itis highly situated in the specifics of the unfoldingevent, is urgent and high-staked, and occurs at theboundary between communities of practice. Becausecognition is distributed, responsibility is shared, andepistemic differences are present, interactions can becontentious and conflict laden.

xpertisecoordination refers to processes that manage knowl-edge and skill interdependencies

we describe two categories ofcoordination practices that ensure effective work out-comes. The first category, which we callexpertise coor-dination practices, represents processes that make itpossible to manage knowledge and skill interdepen-dencies. These processes bring about fast response,superior reconfiguration, efficient knowledge shar-ing, and expertise vetting. Second, because of therapidlyunfoldingtempooftreatmentandthestochas-tic nature of the treatment trajectory,dialogic coordina-tion practicesare used as contextually and temporallysituated responses to occasional trajectory deviation,errors, and general threats to the patient. These dia-logic coordination practices are crucial for ensuringeffective coordination but often require contentiousinteractions across communities of practice. Figure 1presents a coordination-focused model of patienttreatment and describes the circumstances underwhich dialogic coordination practices are called for.

We found that coordination in a trauma settingentails two specific practices.

Based on a practice view, we suggest the followingdefinition ofcoordination: a temporally unfolding andcontextualized process of input regulation and inter-action articulation to realize a collective performance.

Theprimarygoalispatientstabilizationandini-tiating atreatment trajectory—a temporally unfolding

rauma centersare representative of organizational entities that arefaced with unpredictable environmental demands,complexsetsoftechnologies,highcoordinationloads,and the paradoxical need to achieve high reliabilitywhile maintaining efficient operations.

We sug-gest that for environments where knowledge work isinterdisciplinary and highly contextualized, the rele-vant lens is one of practice. Practices emerge from anongoing stream of activities and are enacted throughthe contextualized actions of individuals (Orlikowski2000). These practices are driven by a practical logic,thatis,arecognitionofnoveltaskdemands,emergentsituations,andtheunpredictabilityofevolvingaction.Bourdieu (1990, p. 12) definespracticesas generativeformulas reflecting the modus operandi (manner ofworking) in contrast to the opus operatum (finishedwork).

In knowledge work, several related factors sug-gest the need to reconceptualize coordination.

Usinga practice lens (Brown and Duguid 2001, Orlikowski2000), we suggest that in settings where work iscontextualized and nonroutine, traditional models ofcoordination are insufficient to explain coordinationas it occurs in practice. First, because expertise is dis-tributed and work highly contextualized, expertisecoordination is required to manage knowledge andskill interdependencies. Second, to avoid error andto ensure that the patient remains on a recoveringtrajectory, fast-response cross-boundary coordinationpractices are enacted. Because of the epistemic dis-tance between specialists organized in communitiesofpractice,theselattercoordinationpracticesmagnifyknowledge differences and are partly contentious.

In this paper, we focus on the collective perfor-manceaspectofcoordinationandemphasizethetem-poral unfolding and situated nature of coordinativeaction. We address how knowledge work is coor-dinated in organizations where decisions must bemade rapidly and where errors can be fatal.

The goal of this framework is to envision afuture of crowd work that cansupport more complex, creative, and highly valued work. At the highest level, a platformis needed for managing pools of tasksandworkers. Complex tasks must be decomposed into smaller subtasks, each designed with particular needs and characteristics which must be assignedto appropriate groups of workerswho themselves must be properly motivated, selected(e.g., through reputation), and organized (e.g., through hierarchy). Tasks may be structured through multi-stage workflowsin which workers may collaborateeither synchronously or asynchronously. As part of this, AImay guide (and be guided by) crowd workers. Finally, quality assuranceis needed to ensure each worker’s output is of high quality and fits together.

n human computation, people act as computational components and perform the work that AI systemslack the skillstocomplete

A promising approach that addresses some worker output issues examines the way that workers do their work rather than the output itself, using machine learning and/or visualization to predict the quality of a worker’s output from their behavior [119,120]

Of the research foci, quality control has arguably received the most attention so far. Approaches for quality control largely fall into two camps: up-front task design and post-hoc result analysis. Task design aimsto design tasks that are resistant to low-quality work.

Many tasks worth completing require cooperation –yet crowdsourcing has largely focused on independent work. Distributed teams have always facedchallenges in cultural differences and coordination[60], but crowd collaboration now must createrapport over much shorter timescales(e.g., one hour) and possibly wider culturalor socioeconomic gaps

Finally, it will be amajor research undertaking to invent and describe the tasks and techniques that succeed with synchronous collaboration

The two core challenges for realtime crowdsourcing will be 1) scaling upto increased demand for realtime workers, and 2) making workers efficient enough to collectively generateresultsahead of time deadlines.

Volunteer crowdsourcing platforms have evolved their own hierarchies and decision-making processes [104,156], appropriating techniques from other online communities where appropriate [101]. Most paid approaches have workers make hierarchical decisions collectively: for example, task decomposition and integration[75,80],quality oversight of each others’ contributions[78,100], and leader elections to represent collective opinions[83].

Complex tasks have dependencies, changing requirements, and require multiple types of expertise.

n the sections below, we survey and analyze the 12research focithat comprise our model. First, we consider the future of the work processesand how the workis organized and accomplished. Second, we consider the integration of crowd work and computation, including the symbiosis between human cognition, artificial intelligence(AI), and computationally-enabled crowd platforms. Finally, we consider crowd workers and how we can develop jobs, reputation systems, motivations, and incentives that will benefit them.

Unlike traditional organizations in which workers possess job security and managers can closely supervise and appropriately reward or sanction workers, or distributed computing systems in which processors are usually highly reliable, crowd work poses uniquechallenges for both workers and requestersranging fromjob satisfactiontodirection-setting, coordination, and quality control.

These same requirements exist in distributed computing, in which tasks need to be scheduled so that they can be completed in the correct sequence and in a timely manner, with data being transferred between computing elements appropriately.

However, crowd work can bea double-edged sword,equally capable of enhancing or diminishing the quality of workers’ lives.We maysee echoes of past labor abuses in globally distributed crowd work: extremely low pay for labor,with marketplaces such as Amazon’s Mechanical

We focus this paper on paid,onlinecrowd work, which we define here as the performance of tasks online by distributed crowd workers who are financially compensated by requesters(individuals, groups, or organizations). In this sense, crowd work is a socio-technical work systemconstituted through a set of relationships that connect organizations, individuals, technologies and work activities

Other foundational accounts, like Moglen (1999) and Weber (2004),attended to the emergence of informal hierarchies and governance arrange-ments within communities.

Peer productioncould, Benkler argued, outperform traditional organizational forms underconditions of widespread access to networked communications technologies,a multitude of motivations driving contributions, and non-rival informationcapable of being broken down into granular, modular, and easy-to-integrate

stress-inducing focus on end products

The girls unanimously agree that this is a constant problem in their work and they feel helpless to combat it.

Or more plainly: attention on social media both compensates for and is the logical endpoint of commoditized care work.

But these features also make it ripe for conflict between sex worker activists and anti-trafficking activists who oppose sex work. One of the most frequent attacks on Twitter is that these activists are pimps pretending to be sex workers. This argument defeminizes sex workers into the masculine identity of a pimp and paints them as co-conspirators in trafficking. It’s a form of gendered shaming against female-identified sex workers that pits them over and against victimized women and girls