Proof: How Crowdsourced Election Monitoring Makes a Difference

Curated on Posted on by Stefaan Verhulst

Patrick Meier at iRevolution: “My colleagues Catie Bailard & Steven Livingston have just published the results of their empirical study on the impact of citizen-based crowdsourced election monitoring. Readers of iRevolution may recall that my doctoral dissertation analyzed the use of crowdsourcing in repressive environments and specifically during contested elections. This explains my keen interest in the results of my colleagues’ news data-driven study, which suggests that crowdsourcing does have a measurable and positive impact on voter turnout.

Reclaim Naija

Catie and Steven are “interested in digitally enabled collective action initiatives” spearheaded by “nonstate actors, especially in places where the state is incapable of meeting the expectations of democratic governance.” They are particularly interested in measuring the impact of said initiatives. “By leveraging the efficiencies found in small, incremental, digitally enabled contributions (an SMS text, phone call, email or tweet) to a public good (a more transparent election process), crowdsourced elections monitoring constitutes [an] important example of digitally-enabled collective action.” To be sure, “the successful deployment of a crowdsourced elections monitoring initiative can generate information about a specific political process—information that would otherwise be impossible to generate in nations and geographic spaces with limited organizational and administrative capacity.”

To this end, their new study tests for the effects of citizen-based crowdsourced election monitoring efforts on the 2011 Nigerian presidential elections. More specifically, they analyzed close to 30,000 citizen-generated reports of failures, abuses and successes which were publicly crowdsourced and mapped as part of the Reclaim Naija project. Controlling for a number of factors, Catie and Steven find that the number and nature of crowdsourced reports is “significantly correlated with increased voter turnout.”

In conclusion, the authors argue that “digital technologies fundamentally change information environments and, by doing so, alter the opportunities and constraints that the political actors face.” This new study is an important contribution to the literature and should be required reading for anyone interested in digitally-enabled, crowdsourced collective action. Of course, the analysis focuses on “just” one case study, which means that the effects identified in Nigeria may not occur in other crowdsourced, election monitoring efforts. But that’s another reason why this study is important—it will no doubt catalyze future research to determine just how generalizable these initial findings are.”

Creating a national citizen engagement process for energy policy

Curated on Posted on by Stefaan Verhulst

Paper by Nick Pidgeon et al in the Proceedings of the National Academy of Sciences (PNAS): “This paper examines some of the science communication challenges involved when designing and conducting public deliberation processes on issues of national importance. We take as our illustrative case study a recent research project investigating public values and attitudes toward future energy system change for the United Kingdom. National-level issues such as this are often particularly difficult to engage the public with because of their inherent complexity, derived from multiple interconnected elements and policy frames, extended scales of analysis, and different manifestations of uncertainty. With reference to the energy system project, we discuss ways of meeting a series of science communication challenges arising when engaging the public with national topics, including the need to articulate systems thinking and problem scale, to provide balanced information and policy framings in ways that open up spaces for reflection and deliberation, and the need for varied methods of facilitation and data synthesis that permit access to participants’ broader values. Although resource intensive, national-level deliberation is possible and can produce useful insights both for participants and for science policy.”

Experiments on Crowdsourcing Policy Assessment

Curated on Posted on by Stefaan Verhulst

Paper by John Prpić, Araz Taeihagh, and James Melton Jr for the Oxford Internet Institute IPP2014: Crowdsourcing for Politics and Policy: “Can Crowds serve as useful allies in policy design? How do non-expert Crowds perform relative to experts in the assessment of policy measures? Does the geographic location of non-expert Crowds, with relevance to the policy context, alter the performance of non-experts Crowds in the assessment of policy measures? In this work, we investigate these questions by undertaking experiments designed to replicate expert policy assessments with non-expert Crowds recruited from Virtual Labor Markets. We use a set of ninety-six climate change adaptation policy measures previously evaluated by experts in the Netherlands as our control condition to conduct experiments using two discrete sets of non-expert Crowds recruited from Virtual Labor Markets. We vary the composition of our non-expert Crowds along two conditions: participants recruited from a geographical location directly relevant to the policy context and participants recruited at-large. We discuss our research methods in detail and provide the findings of our experiments.”
Full program of the Oxford Internet Institute IPP2014: Crowdsourcing for Politics and Policy can be found here.

The measurable me: the influence of self-quantification on the online user's decision-making process

Curated on Posted on by Stefaan Verhulst

Paper by Mimmi Sjöklint for the 2014 ACM International Symposium on Wearable Computers: “The advancement of information technology, online accessibility and wearable computing is fostering a new playground for users to engage with quantified data sets. On one hand, the online user is continuously yet passively exposed to different types of quantified data in online interfaces and mobile apps. On the other hand, the user may actively and knowingly be gathering quantified data through ubiquitous sensory devices, such as wearable technology, e.g. the Jawbone UP and Fitbit. In both instances, the user is exposed to versions of self-quantified measures, namely the aggregation and transformation of personally attributed activity into quantified data. This study approaches the adoption of wearables by looking at active and passive self-quantification online and explores how it may influence and support the user’s cognitive processes and subsequent decision-making process.”

Crowdteaching: Supporting Teaching as Designing in Collective Intelligence Communities

Curated on Posted on by Stefaan Verhulst

Paper by Mimi Recker, Min Yuan, and Lei Ye in the International Review of Research in Open and Distant Learning: “The widespread availability of high-quality Web-based content offers new potential for supporting teachers as designers of curricula and classroom activities. When coupled with a participatory Web culture and infrastructure, teachers can share their creations as well as leverage from the best that their peers have to offer to support a collective intelligence or crowdsourcing community, which we dub crowdteaching. We applied a collective intelligence framework to characterize crowdteaching in the context of a Web-based tool for teachers called the Instructional Architect (IA). The IA enables teachers to find, create, and share instructional activities (called IA projects) for their students using online learning resources. These IA projects can further be viewed, copied, or adapted by other IA users. This study examines the usage activities of two samples of teachers, and also analyzes the characteristics of a subset of their IA projects. Analyses of teacher activities suggest that they are engaging in crowdteaching processes. Teachers, on average, chose to share over half of their IA projects, and copied some directly from other IA projects. Thus, these teachers can be seen as both contributors to and consumers of crowdteaching processes. In addition, IA users preferred to view IA projects rather than to completely copy them. Finally, correlational results based on an analysis of the characteristics of IA projects suggest that several easily computed metrics (number of views, number of copies, and number of words in IA projects) can act as an indirect proxy of instructionally relevant indicators of the content of IA projects.”

Smartphone Movements Could Reveal Empty Parking Spots

Curated on Posted on by Stefaan Verhulst

Caleb Garling at MIT Technology Review: “Researchers have come up with a novel way to find parking spots with your smartphone. It promises to be much easier than driving around looking for an empty space, and doesn’t require the installation of pricey sensors or other methods for tracking available spots.
At the State University of New York at Buffalo, researchers built an app called PocketParker that does what they’re calling “pocketsourcing”—essentially, turning smartphones into passive sensors that track the location and movements of other users who’ve installed the app. A remote computer crunches the aggregate user actions and determines the likelihood that a lot has an open space. A paper about PocketParker will be presented at the ubiquitous computing conference UbiComp in Seattle next week.
While some parking lots employ sensors to gather information about capacity, PocketParker works without any such infrastructure. It pulls parking lot data from OpenStreetMap and calculates the number of spaces in a given lot based on its dimensions. During a study, researchers found that they could predict the number of spaces to within 6 percent of the actual number.
The app uses the smartphone’s accelerometer to determine where a user is and gauges whether he’s looking for a parking spot based on his movements. If a user drives slowly through a parking lot without stopping, that signals that the lot is full. If a user displays movements typical of walking and then suddenly speeds up and leaves the lot, that signifies that he likely just got into his car and drove away. The app calculates this in the background. “There should be no interaction required,” says SUNY Buffalo computer science professor and paper coauthor Geoffrey Challen….”

Citizen Science: The Law and Ethics of Public Access to Medical Big Data

Curated on Posted on by Stefaan Verhulst

New Paper by Sharona Hoffman: “Patient-related medical information is becoming increasingly available on the Internet, spurred by government open data policies and private sector data sharing initiatives. Websites such as HealthData.gov, GenBank, and PatientsLikeMe allow members of the public to access a wealth of health information. As the medical information terrain quickly changes, the legal system must not lag behind. This Article provides a base on which to build a coherent data policy. It canvasses emergent data troves and wrestles with their legal and ethical ramifications.
Publicly accessible medical data have the potential to yield numerous benefits, including scientific discoveries, cost savings, the development of patient support tools, healthcare quality improvement, greater government transparency, public education, and positive changes in healthcare policy. At the same time, the availability of electronic personal health information that can be mined by any Internet user raises concerns related to privacy, discrimination, erroneous research findings, and litigation. This Article analyzes the benefits and risks of health data sharing and proposes balanced legislative, regulatory, and policy modifications to guide data disclosure and use.”

Developing Public Policy To Advance The Use Of Big Data In Health Care

Curated on Posted on by Stefaan Verhulst

Paper by Axel Heitmueller et al in Health Affairs:  “The vast amount of health data generated and stored around the world each day offers significant opportunities for advances such as the real-time tracking of diseases, predicting disease outbreaks, and developing health care that is truly personalized. However, capturing, analyzing, and sharing health data is difficult, expensive, and controversial. This article explores four central questions that policy makers should consider when developing public policy for the use of “big data” in health care. We discuss what aspects of big data are most relevant for health care and present a taxonomy of data types and levels of access. We suggest that successful policies require clear objectives and provide examples, discuss barriers to achieving policy objectives based on a recent policy experiment in the United Kingdom, and propose levers that policy makers should consider using to advance data sharing. We argue that the case for data sharing can be won only by providing real-life examples of the ways in which it can improve health care.”

Business Models for Open Innovation: Matching Heterogenous Open Innovation Strategies with Business Model Dimensions

Curated on Posted on by Stefaan Verhulst

New paper by Saebi, Tina and Foss, Nicolai, available at SSRN:  “Research on open innovation suggests that companies benefit differentially from adopting open innovation strategies; however, it is unclear why this is so. One possible explanation is that companies’ business models are not attuned to open strategies. Accordingly, we propose a contingency model of open business models by systematically linking open innovation strategies to core business model dimensions, notably the content, structure, governance of transactions. We further illustrate a continuum of open innovativeness, differentiating between four types of open business models. We contribute to the open innovation literature by specifying the conditions under which business models are conducive to the success of open innovation strategies.”

The Crypto-democracy and the Trustworthy

Curated on Posted on by Stefaan Verhulst

New Paper by Sebastien Gambs, Samuel Ranellucci, and Alain Tapp: “In the current architecture of the Internet, there is a strong asymmetry in terms of power between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud providers, …) and the individuals from which this personal data is issued. In particular, individuals have no choice but to blindly trust that these entities will respect their privacy and protect their personal data. In this position paper, we address this issue by proposing an utopian crypto-democracy model based on existing scientific achievements from the field of cryptography. More precisely, our main objective is to show that cryptographic primitives, including in particular secure multiparty computation, offer a practical solution to protect privacy while minimizing the trust assumptions. In the crypto-democracy envisioned, individuals do not have to trust a single physical entity with their personal data but rather their data is distributed among several institutions. Together these institutions form a virtual entity called the Trustworthy that is responsible for the storage of this data but which can also compute on it (provided first that all the institutions agree on this). Finally, we also propose a realistic proof-of-concept of the Trustworthy, in which the roles of institutions are played by universities. This proof-of-concept would have an important impact in demonstrating the possibilities offered by the crypto-democracy paradigm.”