For the Stop Reset Go project, we are exploring how we achieve a group flow state that can connect us in an experience of deep humanity as we engage in a process of human inner transformation and social outer transformation. The goal of the project is bottom-up whole system change.

The concept of a builders collective is to document what people are already doing to build a world that works for 100% of life.

Mark Wagnon shared this site for personal growth, community development, and global connection when I was talking about an idea to build an app for a Minimalist Journal.

We are all designers

A design brief for a project to reimagine our social architecture. The Design Science Studio is the new Bauhaus, the community, the builders collective who are building a world that works for 100% of life. The Stop Reset Go is the project that is exploring how we imagine, design, and build the kind of world that we want to live in. How do we empower humanity to be the designers who can engage in a process of bottom-up whole system change?

Builders Collective

So, they put together a team of individuals who could collectively imagine the possibilities of building the kind of world where they could all live together in peace. They made a simple declaration:

We are exploring how we imagine, design, and build the future together.

Karimi-Malekabadi, F., Reimer, N. K., Atari, M., Trager, J., Kennedy, B., Graham, J., & Dehghani, M. (2021). Moral Values Predict County-Level COVID-19 Vaccination Rates in the United States [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/z6kxm

Transactive memory is how a group encodes, stores, and shares knowledge. Members of a group may be aware of the portions of knowledge that others possess which can make them more efficient.

How can we link this to Cesar Hidalgo's ideas about the personbyte, etc.?

How would this idea have potentially helped oral cultures?

She uses the example of a trauma resuscitation team helping to shorten hospital stays, but certainly there are many examples in the corporate world where corporate knowledge is helpful in decreasing time scales for particular outcomes.

This is an opportunity for collective action to complement corporate action. Such collective action will be more effective if closely coordinated with individual actors as well as broader umbrella sectors, as in this particular case.

Smith, M. J., Ahmad, A., Arawi, T., Dawson, A., Emanuel, E. J., Garani-Papadatos, T., Ghimire, P., Iliyasu, Z., Lei, R., Mastroleo, I., Mathur, R., Okeibunor, J., Parker, M., Saenz, C., Thomé, B., Upshur, R. E. G., & Voo, T. C. (2021). Top five ethical lessons of COVID-19 that the world must learn. Wellcome Open Research, 6, 17. https://doi.org/10.12688/wellcomeopenres.16568.1

Ghavasieh, A., Nicolini, C., & De Domenico, M. (2020). Statistical physics of complex information dynamics. Physical Review E, 102(5), 052304. https://doi.org/10.1103/PhysRevE.102.052304

Gronfeldt, B., Cichocka, A., Cislak, A., Sternisko, A., & Irem. (2021). A Small Price to Pay: National Narcissism Predicts Readiness to Sacrifice In-group Members to Defend the In-group’s Image [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7fmrx

Il ne s’agirait donc pas de simplement devenir collectivement «plus intelligent» (au sens d’efficace, dans un strict paradigme scientifique et technique pour accélérer le fonctionnement de l’économie), mais aussi réflexif: réfléchir aux conditions de cette société renouvelée, en proie à de nouvelles dynamiques de pouvoir extrêmement concentrées et asymétriques.

Metzler, Hannah, Bernard Rimé, Max Pellert, Thomas Niederkrotenthaler, Anna Di Natale, and David Garcia. “Collective Emotions during the COVID-19 Outbreak.” PsyArXiv, June 8, 2021. https://doi.org/10.31234/osf.io/qejxv.

<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>JHI Blog </span> in Collective Memory - JHI Blog (<time class='dt-published'>05/12/2021 21:55:54</time>)</cite></small>

Drury, J., Reicher, S., & Stott, C. (n.d.). COVID-19 in context: Why do people die in emergencies? It’s probably not because of collective psychology. British Journal of Social Psychology, n/a(n/a). https://doi.org/10.1111/bjso.12393

Ross, J. S. (2021). Covid-19, open science, and the CVD-COVID-UK initiative. BMJ, 373, n898. https://doi.org/10.1136/bmj.n898

Keshmirian, A., Bahrami, B., & Deroy, O. (2021, April 27). Many Heads Are More Utilitarian Than One. https://doi.org/10.31234/osf.io/7e3dc

pls read. u hate neo/liberal representation politics. not identity politics.

Note

Rateau, P., Tavani, J. L., & Delouvée, S. (2021). Social representations of the Coronavirus and causal perception of its origin. The role of reasons for fear. PsyArXiv. https://doi.org/10.31234/osf.io/2dawr

objectif de l'intelligence collective, des humanités numériques comme discipline en communauté

Injonction aux faiseurs de connaissance – autre morceau du programme de l’intelligence collective de Pierre Lévy?

versant éthique?

This reminds me of an interesting study from MIT relating to collective learning that I heard about from Cesar Hidalgo recently.

Lawlor, Jennifer, Zachary Neal, and Kyle Metta. ‘What Is a Coalition? A Systematic Review of Coalitions in Community Psychology’. Preprint. PsyArXiv, 20 August 2020. https://doi.org/10.31234/osf.io/ba4yw.

Taraborelli, D., Taraborelli, D., & Taraborelli, D. (2020, August 5). How the COVID-19 crisis has prompted a revolution in scientific publishing. Fast Company. https://www.fastcompany.com/90537072/how-the-covid-19-crisis-has-prompted-a-revolution-in-scientific-publishing

Bordalo, P., Coffman, K. B., Gennaioli, N., & Shleifer, A. (2020). Older People are Less Pessimistic about the Health Risks of Covid-19 (Working Paper No. 27494; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27494

Briscese, G., Lacetera, N., Macis, M., & Tonin, M. (2020). Compliance with COVID-19 Social-Distancing Measures in Italy: The Role of Expectations and Duration (Working Paper No. 26916; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26916

Martin, G., Hanna, E., & Dingwall, R. (2020). Face masks for the public during Covid-19: An appeal for caution in policy [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/uyzxe

Krumpal, I. (2020). Soziologie in Zeiten der Pandemie [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yqdsu

Schraff, D. (2020). Political trust during the Covid-19 pandemic: Rally around the flag or lockdown effects? [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/pu47c

Thomas W. Malone—COVID-19 and Collective Intelligence (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=W5RfAZMMTPM

Jeff Howe - Crowdsourcing and the Crisis: Collective Intelligence in the Age of Covid-19 (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=POPMMHyIoS0

James Evans—Designing Diversity for Collective Advance (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=1OMmJJz0oF0

Free our knowledge. (n.d.). Retrieved June 30, 2020, from https://www.freeourknowledge.org/pages/about/

ACM Collective Intelligence 2020—Keynotes. (n.d.). YouTube. https://www.youtube.com/channel/UCYecxGPlIg9XryIeXVtsMvA

search engines usually can surface that faster (less cognitive load than recalling what and where you store something) than you retrieve it in your second brain (abundance info, do can always retrieve from external source in a JIT fashion)

Long read: Cultural evolution, Covid-19, and preparing for what’s next. (2020, April 22). LSE Business Review. https://blogs.lse.ac.uk/businessreview/2020/04/22/long-read-cultural-evolution-covid-19-and-preparing-for-whats-next/

World Pandemic Research Network

where's the line though? every output generates a feedback loop with the hivemind, turning into input to ourselves with our cracking, overwhelmed, filters

it's unrealistic to wish everyone to see jokes are jokes, to rely on journalists to generate unbiased facts, and politicians as self serving leeches, err that's my bias speaking

Pescetelli, N., Cebrian, M., & Rahwan, I. (2020, February 10). Real-time Internet Control of Situated Human Agents. https://doi.org/10.31234/osf.io/xn7sr

Sternisko, A., Cichocka, A., Cislak, A., & Van Bavel, J. J. (2020). Collective narcissism predicts the belief and dissemination of conspiracy theories during the COVID-19 pandemic [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/4c6av

Williams, A. E. (2020, April 20). The Global Response to COVID-19 as an Example of a One-Sided Problem Definition in the Absence of General Collective Intelligence. https://doi.org/10.31234/osf.io/emgxc

Alshaabi, T., et al. (2020 March 27). How the world's collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter. Cornell University. arXiv:2003.12614

Pastor-Escuredo, D., & Tarazona, C. (2020). Characterizing information leaders in Twitter during COVID-19 crisis. ArXiv:2005.07266 [Physics]. http://arxiv.org/abs/2005.07266

Hargreaves, J., Davey, C., Hargreaves, J., Davey, C., Auerbach, J., Blanchard, J., Bond, V., Bonell, C., Burgess, R., Busza, J., Colbourn, T., Cowan, F., Doyle, A., Hakim, J., Hensen, B., Hosseinipour, M., Lin, L., Johnson, S., Masuka, N., … Yekeye, R. (2020). Three lessons for the COVID-19 response from pandemic HIV. The Lancet HIV, S2352301820301107. https://doi.org/10.1016/S2352-3018(20)30110-7

Rahmani P, Peruani F, Romanczuk P (2020) Flocking in complex environments—Attention trade-offs in collective information processing. PLoS Comput Biol 16(4): e1007697. https://doi.org/10.1371/journal.pcbi.1007697

Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaid, M., & Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences, 201917687. https://doi.org/10.1073/pnas.1917687117

Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), annurev-soc-121919-054621. https://doi.org/10.1146/annurev-soc-121919-054621

Bento, A. I., Nguyen, T., Wing, C., Lozano-Rojas, F., Ahn, Y.-Y., & Simon, K. (2020). Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases. Proceedings of the National Academy of Sciences, 202005335. https://doi.org/10.1073/pnas.2005335117

Pescetelli, N., Rutherford, A., Kao, A., & Rahwan, I. (2020). Pescetelli_et_al_Collective learning in news consumption. https://doi.org/10.31234/osf.io/w6nc5

Liu, L., Wang, X., Tang, S., & Zheng, Z. (2020). Complex social contagion induces bistability on multiplex networks. ArXiv:2005.00664 [Physics]. http://arxiv.org/abs/2005.00664

Pieper, D. (2020, May 1). Challenging social systems under the threat of pollution: Replication and extension of Eadeh and Chang (2019). Retrieved from psyarxiv.com/axbj4

Nanni, M., Andrienko, G., Boldrini, C., Bonchi, F., Cattuto, C., Chiaromonte, F., Comandé, G., Conti, M., Coté, M., Dignum, F., Dignum, V., Domingo-Ferrer, J., Giannotti, F., Guidotti, R., Helbing, D., Kertesz, J., Lehmann, S., Lepri, B., Lukowicz, P., … Vespignani, A. (2020). Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. ArXiv:2004.05222 [Cs]. http://arxiv.org/abs/2004.05222

Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19

fonction de l’espace public: un appareil critique (la critique est productrice d’espace public).

le pouvoir, pour maintenir sa légitimité, doit être exposé à la sphère publique et se montrer à lui avec transparence; il doit pouvoir être challengé; s’il ne résiste pas à la critique publique, il ne mérite pas d’être en place.

la possibilité de challenger l’instance publique est comparable à la publication des protocoles de sécurité utilisées dans le domaine public (ex. SSL/TLS): la sécurité des contenus encryptés tirent justement leur robustesse du fait que leur algorithme est public; quiconque pourrait le challenger à tout moment, si bien qu’on s’assure d’en éliminer toutes les failles (et l’intelligence collective peut être mise à contribution, le cas échéant).

Sense Collective's TotemSDK brings together tools, protocols, platform integrations and best practices for extending collective intelligence beyond our current capabilities. A number of cryptographic primitives have emerged which support the amazing work of projects like the NextNow Collaboratory in exciting ways that help to upgrade the general purpose social computing substrate which make tools like hypothes.is so valuable.

Sense Collective's Climate Tagger API and Pool Party Semantic Web plug-in are perfectly suited to support The Wilson Center's metadata schema project. Creating a common metadata schema that is used across multiple organizations working within the same domain, with similar (and overlapping) data and data types, is an essential step towards realizing collective intelligence. There is significant redundancy that consumes limited resources as organizations often perform the same type of data structuring. Interoperability issues between organizations, their metadata semantics and serialization methods, prevent cumulative progress as a community. Sense Collective's MetaGrant program is working to provide a shared infastructure for NGO's and social impact investment funds and social impact bond programs to help rapidly improve the problems that are being solved by this awesome project of The Wilson Center. Now let's extend the coordinated metadata semantics to 1000 more organizations and incentivize the citizen science volunteers who make this possible, with a closer connection to the local benefits they produce through their efforts. With integration into Social impact Bond programs and public/private partnerships, we are able to incentivize collective action in ways that match the scope and scale of the problems we face.

Travailler à partir d’un matériau (ici, une photo).

Collective Intelligence Playground

Engelbart explicitly noted that what he was trying to do was not just hack culture, which is what significant innovations accomplish, but to hack the process by which biological and cultural co-evolution has bootstrapped itself to this point. Culture used the capabilities provided by biological evolution -- language, thumbs, etc. -- to improve human ways of living much faster than biological evolution can do, by not just inventing, but passing along to each other and future generations the knowledge of what was invented and how to invent. Engelbart proposes an audio-visual-tactile interface to computing as a tool for consciously accelerating the scope and power of individual and collective intelligence.

To me, this is the most prescient of Engelbart's future visions, and the seed for future study of culture-technology co-evolution. I talked with Engelbart about this passage over the years and we agreed that although the power of the artifacts, from RAM to CPU speed to network bandwidth, had improved by the billionfold since 1962, the "softer" parts of the formula -- the language, methodology, and training -- have not advanced so much. Certainly language, training methods and pedagogy, and collaborative strategies have evolved with the growth and spread of digital media, but are still lagging. H/LAMT interests me even more today than it did thirty years ago because Engelbart unknowingly forecast the fundamental elements of what has come to be called cultural-biological co-evolution. I gave a TED talk in 2005, calling for an interdisciplinary study of human cooperation -- and obstacles to cooperation. It seems that in recent years an interdisciplinary understanding has begun to emerge. Joseph Henrich at Harvard, for one, in his recent book, The Secret of Our Success, noted:

Drawing insights from lost European Explorers, clever chimpanzees, hunter-gatherers, cultural neuroscience, ancient bones and the human genome, Henrich shows that it’s not our general intelligence, innate brain power, or specialized mental abilities that explain our success. Instead, it’s our collective brains, which arise from a combination of our ability to learn selectively from each and our sociality. Our collective brains, which often operate outside of any individual’s conscious awareness, gradually produce increasingly complex, nuanced and subtle technological, linguistic and social products over generations.

Tracking this back into the mist of our evolutionary past, and to the remote corners of the globe, Henrich shows how this non-genetic system of cultural inheritance has long driven human genetic evolution. By producing fire, cooking, water containers, tracking know-how, plant knowledge, words, hunting strategies and projectiles, culture-driven genetic evolution expanded our brains, shaped our anatomy and physiology, and influenced our psychology, making us into the world’s only living cultural species. Only by understanding cultural evolution, can we understand human genetic evolution.

Henrich, Boyd, and RIcherson wrote, about the social fundamentals that distinguish human culture's methods of evolving collective intelligence in The Origin and Evolution of Culture:

Surely, without punishment, language, technology, individual intelligence and inventiveness, ready establishment of reciprocal arrangements, prestige systems and solutions to games of coordination, our societies would take on a distinctly different cast. Thus, a major constraint on explanations of human sociality is its systemic structure

Knowledge is social.

Collective bargaining in action. And, more importantly perhaps, this puts Unions as a power to the people backstop. The action of Federal Employees, unionized, who could just stop showing up without fear of being fired is what we should be selling.

Connects crisis informatics research with collective behavior.

I wonder though does Neal define collective behavior and collection action differently?

Look at these citations:

Quarantelli, E.L. 1985. Emergent Citizen Groups in Disaster Preparedness and Recovery Activities. Final Report 33. Newark, Delaware: Disaster Research Center, University of Delaware

Turner, Ralph and Lewis Killian. 1987. Collective Behavior. Third Edition. Engle­wood Cliffs, New Jersey: Prentice Hall.

Weller, Jack M. and E. L. Quarantelli. 1973. "Neglected Characteristics of Collec­tive Behavior," American Journal of Sociology 79:665-85.

Wenger, Dennis. 1987. "Collective Behavior and Disaster Research." Pp. 213-237 in Sociology of Disasters, edited by R. Dynes, B. De Marchi and C. Pelanda. Milan, Italy: Franco Angeli.

Neal connects concerns about disaster-driven social change and the natural desire for people to respond via some collective action impulse.

Nice segue into SBTF as collection action motivated by social change

Interesting frame of reference for this study that also helps to unpack the contribution of the SBTF research.

Perhaps Elinor Ostrom's work could be helpful here too.

The author further identify the true meaning of transhumanism as it is now focusing more of a mesh making all humans quite uniform, taking away culture and amplifying the idea of the collective being a single organism. The authors are more so repeating themselves as it is similar to previous points. The repetition may be there to reinforce the claim of the collective being a single organism. It is rather interesting that it diverse from promoting individualism where people gets to have their own culture and political ideologies, the author is effectively saying that humans should not have a mind of their own but interlinked with others to move within a direction with efficiency.

Diverse labor force is also a central principle of effective crowdsourcing and collective intelligence.

Collective intelligence definition.

Per the authors, "collective intelligence is a superset of social computing and crowdsourcing, because both are defined in terms of social behavior."

Collective intelligence is differentiated from human computation because the latter doesn't require a group.

It is differentiated from crowdsourcing because it doesn't require a public crowd and it can happen without an open call.

How data structure influences value systems and social practices is a much-needed topic of inquiry.

Big, sweeping statement that helps frame how big data could be better conceptualized as a complex, socially contextualized, temporal artifact.

I don't understand what Burns is arguing here. Who is he referring to claims that DHN is a "savior" or "the solution" to crisis response?

"Big data should therefore be be conceptualized as a framing of what can be known about a humanitarian crisis, and how one is able to grasp that knowledge; in short, it is an epistemology. This epistemology privileges knowledges and knowledge- based practices originating in remote geographies and de- emphasizes the connections between multiple knowledges.... Put another way, this configuration obscures the funding, resource, and skills constraints causing imperfect humanitarian response, instead positing volunteered labor as ‘the solution.’ This subjectivity formation carves a space in which digital humanitarians are necessary for effective humanitarian activities." (Burns 2015: 9–10)

Is it "a lack of relational sense of responsibility" in crisis response (state vs private sector vs public) or is it the wicked problem of power, class, social hierarchies, etc.?

"... ways of framing data and correlations that elicit a greater sense of responsibility and commitment."

That could have a temporal component to it to position urgency, timescape, horizon, etc.

This critique addresses surveillance state concerns about glue-ing datasets together to form a broader understanding of aggregate social behavior without the necessary constraints/warnings about social contexts and discontinuity between data.

Skimmed the Ruppert paper, sadly doesn't engage with time and topologies.

Beyond this quote, Chandler (in engaging crisis/disaster scenarios) argues that Big Data may be more appropriately framed as community reflexive knowledge than causal knowledge. That's an interesting idea.

*"Thus, It would be more useful to see Big Data as reflexive knowledge rather than as causal knowledge. Big Data cannot help explain global warming but it can enable individuals and household to measure their own energy consumption through the datafication of household objects and complex production and supply chains. Big Data thereby datafies or materialises an individual or community’s being in the world. This reflexive approach works to construct a pluralised and multiple world of self-organising and adaptive processes. The imaginary of Big Data is that the producers and consumers of knowledge and of governance would be indistinguishable; where both knowing and governing exist without external mediation, constituting a perfect harmonious and self-adapting system: often called ‘community resilience’. In this discourse, increasingly articulated by governments and policy-makers, knowledge of causal connections is no longer relevant as communities adapt to the real-time appearances of the world, without necessarily understanding them."

"Rather than engaging in external understandings of causality in the world, Big Data works on changing social behaviour by enabling greater adaptive reflexivity. If, through Big Data, we could detect and manage our own biorhythms and know the effects of poor eating or a lack of exercise, we could monitor our own health and not need costly medical interventions. Equally, if vulnerable and marginal communities could ‘datafy’ their own modes of being and relationships to their environments they would be able to augment their coping capacities and resilience without disasters or crises occurring. In essence, the imaginary of Big Data resolves the essential problem of modernity and modernist epistemologies, the problem of unintended consequences or side-effects caused by unknown causation, through work on the datafication of the self in its relational-embeddedness.42 This is why disasters in current forms of resilience thinking are understood to be ‘transformative’: revealing the unintended consequences of social planning which prevented proper awareness and responsiveness. Disasters themselves become a form of ‘datafication’, revealing the existence of poor modes of self-governance."*

Downloaded Chandler paper. Cites Meier quite a bit.

Burns criticism of humanitarian response as not truly collaborative and an abdication of the state's responsibility for social welfare to the private sector.

Participatory-design and community-centered design could be very helpful in this regard but this argument seems overstated.

Evokes concerns about "distant suffering" (see: Chouliaraki, 2008): Who gets to share? What community? Refugees are not homogeneous.

The dichotomy between state-led response vs community-coordinated response as the only ways to deliver aid seems unnecessarily limited.

It can be both and other models/new ideas.

Conflict- and persecution-driven humanitarian needs are often rife with complexity and receive scant attention outside of the humanitarian INGO sector.

Cited paper references social media and DHN work.

Dataveillance definition.

The supposed benefits for crisis management don't correspond to the earlier criticisms about data quality, loss of contextualization, and predictive analytics accuracy.

The following paragraph clears up some of the overly optimistic promises. Perhaps this section is simply overstated for rhetorical purposes.

I understand the desire to use a topical hook to explain a complex topic but referring to the highly contentious Snowden scandal as a frame seems risky (alienating) and could potentially undermine an important argument about the surveillance state should new revelations be revealed about his motives/credibility.

If the goal is to improve rapid/efficient response to those in need, is it necessarily only a dichotomy of top-down institutional action vs private sector/market-driven reaction? Surely, we can do better than this.

Data/predictive analytics abuses by the private sector are legion.

How does social construction vs technological determinism fit here? In what ways are the real traumas suffered by crisis-affected people not being taken into account during the response/relief/resiliency phases?

The shift from INGO emergency response/logistics to state-sponsored, individualized resilience via the private sector seems profound here.

There's also a subtle temporal element here of surveilling need and collecting data over time.

Again, raises serious questions about the use of predictive analytics, data quality/classification, and PII ethics.

This seems like a terribly naive conclusion about mass data collection by the state.

Also:

"Yet even if capacities to analyse the haystack for needles more adequately were available, there would be questions about the quality of the haystack, and the meaning of analysis. For ‘Big Data is not self-explanatory’ (Bollier 2010: 13, in boyd and Crawford 2012). Neither is big data necessarily good data in terms of quality or relevance (Lesk 2013: 87) or complete data (boyd and Crawford 2012)."

Primary problems to resolve regarding the use of "big data" in humanitarian contexts: dataset size/sample, predictive analytics are contrary to human behavior, and ethical abuses of PII.

Data collection process for refugees is underscored as threat surveillance, whether it is intended or not.

Design implication for ICT4D and ICT for humanitarian response -- moving beyond controlled environment surveillance to ubiquitous and omnipresent.

The Information is Aid perspective that drives my research agenda.

A finer point on threat surveillance that stems from how classifications and categories are framed.

This also gets at post-colonial interpretations of people, places, and events.

See: Winner, Do Artifacts Have Politics? See: Bowker and Star, Sorting things out: Classification and its consequences. See: Irani, Post-Colonial Computing

Data is used by the state to mobilize resources for protective services (border management and immigration/asylum systems) and data is used to count/track refugees in order to provision assistance.

Datafication definition

It also incorporates metadata as well as information gleaned from typical sources.

The promise and peril of gathering collective intelligence, surveillance, and capturing big data during humanitarian crises.

Benkler makes a really bold statement here about how peer production differs from collective intelligence. Not sure I buy this argument.

Brabner on crowdsourcing:

Not all collective intelligence is peer production.

Peer production must adhere to values: de-centralized control, broad range of motives/incentives and FLOSS/creative commons rights.

Focus of related work in collective intelligence studies:

• organizational governance • motives • product quality

Benkler argues that the process, motives, and cultural norms of online network-driven knowledge work are different than systems previously studied and should be re-evaluated.