The GovLab Selected Readings on Crowdsourcing Expertise

As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. In this edition, we explore the literature on Crowdsourcing Expertise. To suggest additional readings on this or any other topic, please email biblio@thegovlab.org.

crowdsourcing

Crowdsourcing enables leaders and citizens to work together to solve public problems in new and innovative ways. New tools and platforms enable citizens with differing levels of knowledge, expertise, experience and abilities to collaborate and solve problems together. Identifying experts, or individuals with specialized skills, knowledge or abilities with regard to a specific topic, and incentivizing their participation in crowdsourcing information, knowledge or experience to achieve a shared goal can enhance the efficiency and effectiveness of problem solving.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Börner, Katy, Michael Conlon, Jon Corson-Rikert, and Ying Ding. “VIVO: A Semantic Approach to Scholarly Networking and Discovery.” Synthesis Lectures on the Semantic Web: Theory and Technology 2, no. 1 (October 17, 2012): 1–178. http://bit.ly/17huggT.

  • This e-book “provides an introduction to VIVO…a tool for representing information about research and researchers — their scholarly works, research interests, and organizational relationships.”
  • VIVO is a response to the fact that, “Information for scholars — and about scholarly activity — has not kept pace with the increasing demands and expectations. Information remains siloed in legacy systems and behind various access controls that must be licensed or otherwise negotiated before access. Information representation is in its infancy. The raw material of scholarship — the data and information regarding previous work — is not available in common formats with common semantics.”
  • Providing access to structured information on the work and experience of a diversity of scholars enables improved expert finding — “identifying and engaging experts whose scholarly works is of value to one’s own. To find experts, one needs rich data regarding one’s own work and the work of potential related experts. The authors argue that expert finding is of increasing importance since, “[m]ulti-disciplinary and inter-disciplinary investigation is increasingly required to address complex problems. 

Bozzon, Alessandro, Marco Brambilla, Stefano Ceri, Matteo Silvestri, and Giuliano Vesci. “Choosing the Right Crowd: Expert Finding in Social Networks.” In Proceedings of the 16th International Conference on Extending Database Technology, 637–648. EDBT  ’13. New York, NY, USA: ACM, 2013. http://bit.ly/18QbtY5.

  • This paper explores the challenge of selecting experts within the population of social networks by considering the following problem: “given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need?”
  • The authors come to the following conclusions:
    • “profile information is generally less effective than information about resources that they directly create, own or annotate;
    • resources which are produced by others (resources appearing on the person’s Facebook wall or produced by people that she follows on Twitter) help increasing the assessment precision;
    • Twitter appears the most effective social network for expertise matching, as it very frequently outperforms all other social networks (either combined or alone);
    • Twitter appears as well very effective for matching expertise in domains such as computer engineering, science, sport, and technology & games, but Facebook is also very effective in fields such as locations, music, sport, and movies & tv;
    • surprisingly, LinkedIn appears less effective than other social networks in all domains (including computer science) and overall.”

Brabham, Daren C. “The Myth of Amateur Crowds.” Information, Communication & Society 15, no. 3 (2012): 394–410. http://bit.ly/1hdnGJV.

  • Unlike most of the related literature, this paper focuses on bringing attention to the expertise already being tapped by crowdsourcing efforts rather than determining ways to identify more dormant expertise to improve the results of crowdsourcing.
  • Brabham comes to two central conclusions: “(1) crowdsourcing is discussed in the popular press as a process driven by amateurs and hobbyists, yet empirical research on crowdsourcing indicates that crowds are largely self-selected professionals and experts who opt-in to crowdsourcing arrangements; and (2) the myth of the amateur in crowdsourcing ventures works to label crowds as mere hobbyists who see crowdsourcing ventures as opportunities for creative expression, as entertainment, or as opportunities to pass the time when bored. This amateur/hobbyist label then undermines the fact that large amounts of real work and expert knowledge are exerted by crowds for relatively little reward and to serve the profit motives of companies. 

Dutton, William H. Networking Distributed Public Expertise: Strategies for Citizen Sourcing Advice to Government. One of a Series of Occasional Papers in Science and Technology Policy, Science and Technology Policy Institute, Institute for Defense Analyses, February 23, 2011. http://bit.ly/1c1bpEB.

  • In this paper, a case is made for more structured and well-managed crowdsourcing efforts within government. Specifically, the paper “explains how collaborative networking can be used to harness the distributed expertise of citizens, as distinguished from citizen consultation, which seeks to engage citizens — each on an equal footing.” Instead of looking for answers from an undefined crowd, Dutton proposes “networking the public as advisors” by seeking to “involve experts on particular public issues and problems distributed anywhere in the world.”
  • Dutton argues that expert-based crowdsourcing can be successfully for government for a number of reasons:
    • Direct communication with a diversity of independent experts
    • The convening power of government
    • Compatibility with open government and open innovation
    • Synergy with citizen consultation
    • Building on experience with paid consultants
    • Speed and urgency
    • Centrality of documents to policy and practice.
  • He also proposes a nine-step process for government to foster bottom-up collaboration networks:
    • Do not reinvent the technology
    • Focus on activities, not the tools
    • Start small, but capable of scaling up
    • Modularize
    • Be open and flexible in finding and going to communities of experts
    • Do not concentrate on one approach to all problems
    • Cultivate the bottom-up development of multiple projects
    • Experience networking and collaborating — be a networked individual
    • Capture, reward, and publicize success.

King, Andrew and Karim R. Lakhani. “Using Open Innovation to Identify the Best Ideas.” MIT Sloan Management Review, September 11, 2013. http://bit.ly/HjVOpi.

  • In this paper, King and Lakhani examine different methods for opening innovation, where, “[i]nstead of doing everything in-house, companies can tap into the ideas cloud of external expertise to develop new products and services.”
  • The three types of open innovation discussed are: opening the idea-creation process, competitions where prizes are offered and designers bid with possible solutions; opening the idea-selection process, ‘approval contests’ in which outsiders vote to determine which entries should be pursued; and opening both idea generation and selection, an option used especially by organizations focused on quickly changing needs.

Noveck, Beth Simone. “‘Peer to Patent’: Collective Intelligence, Open Review, and Patent Reform.” Harvard Journal of Law & Technology 20, no. 1 (Fall 2006): 123–162. http://bit.ly/HegzTT.

  • This law review article introduces the idea of crowdsourcing expertise to mitigate the challenge of patent processing. Noveck argues that, “access to information is the crux of the patent quality problem. Patent examiners currently make decisions about the grant of a patent that will shape an industry for a twenty-year period on the basis of a limited subset of available information. Examiners may neither consult the public, talk to experts, nor, in many cases, even use the Internet.”
  • Peer-to-Patent, which launched three years after this article, is based on the idea that, “The new generation of social software might not only make it easier to find friends but also to find expertise that can be applied to legal and policy decision-making. This way, we can improve upon the Constitutional promise to promote the progress of science and the useful arts in our democracy by ensuring that only worth ideas receive that ‘odious monopoly’ of which Thomas Jefferson complained.”

Ober, Josiah. “Democracy’s Wisdom: An Aristotelian Middle Way for Collective Judgment.” American Political Science Review 107, no. 01 (2013): 104–122. http://bit.ly/1cgf857.

  • In this paper, Ober argues that, “A satisfactory model of decision-making in an epistemic democracy must respect democratic values, while advancing citizens’ interests, by taking account of relevant knowledge about the world.”
  • Ober describes an approach to decision-making that aggregates expertise across multiple domains. This “Relevant Expertise Aggregation (REA) enables a body of minimally competent voters to make superior choices among multiple options, on matters of common interest.”

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Did we miss anything? Please submit reading recommendations to biblio@thegovlab.org or in the comments below.

 

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