Eitan D. Hersh: “For many citizens, participation in politics is not motivated by civic duty or selfinterest, but by hobbyism: the objective is self-gratification. I offer a theory of political hobbyism, situate the theory in existing literature, and define and distinguish the hobbyist motivation from its alternatives. I argue that the prevalence of political hobbyism depends on historical conditions related to the nature of leisure time, the openness of the political process to mass participation, and the level of perceived threat. I articulate an empirical research agenda, highlighting how poli-hobbyism can help explain characteristics of participants, forms of participation, rates of participation, and the nature of partisanship. Political hobbyism presents serious problems for a functioning democracy, including participants confusing high stakes for low stakes, participation too focused on the gratifying aspects of politics, and unnecessarily potent partisan rivalries….(More)”
Countable
Countable: “Why does it have to be so hard to understand what our lawmakers are up to?
With Countable, it doesn’t.
Countable makes it quick and easy to understand the laws Congress is considering. We also streamline the process of contacting your lawmaker, so you can tell them how you want them to vote on bills under consideration.
You can use Countable to:
- Read clear and succinct summaries of upcoming and active legislation.
- Directly tell your lawmakers how to vote on those bills by clicking “Yea” or “Nay”.
- Follow up on how your elected officials voted on bills, so you can hold them accountable in the next election cycle….(More)”
Data innovation: where to start? With the road less taken
Giulio Quaggiotto at Nesta: “Over the past decade we’ve seen an explosion in the amount of data we create, with more being captured about our lives than ever before. As an industry, the public sector creates an enormous amount of information – from census data to tax data to health data. When it comes to use of the data however, despite many initiatives trying to promote open and big data for public policy as well as evidence-based policymaking, we feel there is still a long way to go.
Why is that? Data initiatives are often created under the assumption that if data is available, people (whether citizens or governments) will use it. But this hasn’t necessarily proven to be the case, and this approach neglects analysis of power and an understanding of the political dynamics at play around data (particularly when data is seen as an output rather than input).
Many data activities are also informed by the ‘extractive industry’ paradigm: citizens and frontline workers are seen as passive ‘data producers’ who hand over their information for it to be analysed and mined behind closed doors by ‘the experts’.
Given budget constraints facing many local and central governments, even well intentioned initiatives often take an incremental, passive transparency approach (i.e. let’s open the data first then see what happens), or they adopt a ‘supply/demand’ metaphor to data provision and usage…..
As a response to these issues, this blog series will explore the hypothesis that putting the question of citizen and government agency – rather than openness, volume or availability – at the centre of data initiatives has the potential to unleash greater, potentially more disruptive innovation and to focus efforts (ultimately leading to cost savings).
Our argument will be that data innovation initiatives should be informed by the principles that:
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People closer to the problem are the best positioned to provide additional context to the data and potentially act on solutions (hence the importance of “thick data“).
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Citizens are active agents rather than passive providers of ‘digital traces’.
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Governments are both users and providers of data.
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We should ask at every step of the way how can we empower communities and frontline workers to take better decisions over time, and how can we use data to enhance the decision making of every actor in the system (from government to the private sector, from private citizens to social enterprises) in their role of changing things for the better… (More)
The Wisdom of the Many in Global Governance: An Epistemic-Democratic Defence of Diversity and Inclusion
Paper by Stevenson, H. : “Over the past two decades, a growing body of literature has highlighted moral reasons for taking global democracy seriously. This literature justifies democracy on the grounds of its intrinsic value. But democracy also has instrumental value: the rule of the many is epistemically superior to the rule of one or the rule of the few. This paper draws on the tradition of epistemic democracy to develop an instrumentalist justification for democratizing global governance. The tradition of epistemic democracy is enjoying a renaissance within political theory and popular non-fiction, yet its relevance for international relations remains unexplored. I develop an epistemic-democratic framework for evaluating political institutions, which is constituted by three principles. The likelihood of making correct decisions within institutions of global governance will be greater when (1) human development and capacity for participation is maximised; (2) the internal cognitive diversity of global institutions is maximised; and (3) public opportunities for sharing objective and subjective knowledge are maximised. Applying this framework to global governance produces a better understanding of the nature and extent of the ‘democratic deficit’ of global governance, as well as the actions required to address this deficit….(More)”
Ethical Reasoning in Big Data
Book edited by Collmann, Jeff, and Matei, Sorin Adam: “This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public’s trust in the abilities of particular scientists and scientific institutions constitutes one of this book’s major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest – sometimes implicitly – a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging.
This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Lastly, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature. …(More)”
Addressing the ‘doctrine gap’: professionalising the use of Information Communication Technologies in humanitarian action
Nathaniel A. Raymond and Casey S. Harrity at HPN: “This generation of humanitarian actors will be defined by the actions they take in response to the challenges and opportunities of the digital revolution. At this critical moment in the history of humanitarian action, success depends on humanitarians recognising that the use of information communication technologies (ICTs) must become a core competency for humanitarian action. Treated in the past as a boutique sub-area of humanitarian practice, the central role that they now play has made the collection, analysis and dissemination of data derived from ICTs and other sources a basic skill required of humanitarians in the twenty-first century. ICT use must now be seen as an essential competence with critical implications for the efficiency and effectiveness of humanitarian response.
Practice in search of a doctrine
ICT use for humanitarian response runs the gamut from satellite imagery to drone deployment; to tablet and smartphone use; to crowd mapping and aggregation of big data. Humanitarian actors applying these technologies include front-line responders in NGOs and the UN but also, increasingly, volunteers and the private sector. The rapid diversification of available technologies as well as the increase in actors utilising them for humanitarian purposes means that the use of these technologies has far outpaced the ethical and technical guidance available to practitioners. Technology adoption by humanitarian actors prior to the creation of standards for how and how not to apply a specific tool has created a largely undiscussed and unaddressed ‘doctrine gap’.
Examples of this gap are, unfortunately, many. One such is the mass collection of personally identifiable cell phone data by humanitarian actors as part of phone surveys and cash transfer programmes. Although initial best practice and lessons learned have been developed for this method of data collection, no common inter-agency standards exist, nor are there comprehensive ethical frameworks for what data should be retained and for how long, and what data should be anonymised or not collected in the first place…(More)”
Open Data Supply: Enriching the usability of information
Report by Phoensight: “With the emergence of increasing computational power, high cloud storage capacity and big data comes an eager anticipation of one of the biggest IT transformations of our society today.
Open data has an instrumental role to play in our digital revolution by creating unprecedented opportunities for governments and businesses to leverage off previously unavailable information to strengthen their analytics and decision making for new client experiences. Whilst virtually every business recognises the value of data and the importance of the analytics built on it, the ability to realise the potential for maximising revenue and cost savings is not straightforward. The discovery of valuable insights often involves the acquisition of new data and an understanding of it. As we move towards an increasing supply of open data, technological and other entrepreneurs will look to better utilise government information for improved productivity.
This report uses a data-centric approach to examine the usability of information by considering ways in which open data could better facilitate data-driven innovations and further boost our economy. It assesses the state of open data today and suggests ways in which data providers could supply open data to optimise its use. A number of useful measures of information usability such as accessibility, quantity, quality and openness are presented which together contribute to the Open Data Usability Index (ODUI). For the first time, a comprehensive assessment of open data usability has been developed and is expected to be a critical step in taking the open data agenda to the next level.
With over two million government datasets assessed against the open data usability framework and models developed to link entire country’s datasets to key industry sectors, never before has such an extensive analysis been undertaken. Government open data across Australia, Canada, Singapore, the United Kingdom and the United States reveal that most countries have the capacity for improvements in their information usability. It was found that for 2015 the United Kingdom led the way followed by Canada, Singapore, the United States and Australia. The global potential of government open data is expected to reach 20 exabytes by 2020, provided governments are able to release as much data as possible within legislative constraints….(More)”
The Evolution of Wikipedia’s Norm Network
Bradi Heaberlin and Simon DeDeo at Future Internet: “Social norms have traditionally been difficult to quantify. In any particular society, their sheer number and complex interdependencies often limit a system-level analysis. One exception is that of the network of norms that sustain the online Wikipedia community. We study the fifteen-year evolution of this network using the interconnected set of pages that establish, describe, and interpret the community’s norms. Despite Wikipedia’s reputation for ad hocgovernance, we find that its normative evolution is highly conservative. The earliest users create norms that both dominate the network and persist over time. These core norms govern both content and interpersonal interactions using abstract principles such as neutrality, verifiability, and assume good faith. As the network grows, norm neighborhoods decouple topologically from each other, while increasing in semantic coherence. Taken together, these results suggest that the evolution of Wikipedia’s norm network is akin to bureaucratic systems that predate the information age….(More)”
How Big Data Creates False Confidence
Jesse Dunietz at Nautilus: “…A feverish push for “big data” analysis has swept through biology, linguistics, finance, and every field in between. Although no one can quite agree how to define it, the general idea is to find datasets so enormous that they can reveal patterns invisible to conventional inquiry. The data are often generated by millions of real-world user actions, such as tweets or credit-card purchases, and they can take thousands of computers to collect, store, and analyze. To many companies and researchers, though, the investment is worth it because the patterns can unlock information about anything from genetic disorders to tomorrow’s stock prices.
But there’s a problem: It’s tempting to think that with such an incredible volume of data behind them, studies relying on big data couldn’t be wrong. But the bigness of the data can imbue the results with a false sense of certainty. Many of them are probably bogus—and the reasons why should give us pause about any research that blindly trusts big data.
In the case of language and culture, big data showed up in a big way in 2011, when Google released itsNgrams tool. Announced with fanfare in the journal Science, Google Ngrams allowed users to search for short phrases in Google’s database of scanned books—about 4 percent of all books ever published!—and see how the frequency of those phrases has shifted over time. The paper’s authors heralded the advent of “culturomics,” the study of culture based on reams of data and, since then, Google Ngrams has been, well, largely an endless source of entertainment—but also a goldmine for linguists, psychologists, and sociologists. They’ve scoured its millions of books to show that, for instance, yes, Americans are becoming more individualistic; that we’re “forgetting our past faster with each passing year”; and that moral ideals are disappearing from our cultural consciousness.
The problems start with the way the Ngrams corpus was constructed. In a study published last October, three University of Vermont researchers pointed out that, in general, Google Books includes one copy of every book. This makes perfect sense for its original purpose: to expose the contents of those books to Google’s powerful search technology. From the angle of sociological research, though, it makes the corpus dangerously skewed….
Even once you get past the data sources, there’s still the thorny issue of interpretation. Sure, words like “character” and “dignity” might decline over the decades. But does that mean that people care about morality less? Not so fast, cautions Ted Underwood, an English professor at the University of Illinois, Urbana-Champaign. Conceptions of morality at the turn of the last century likely differed sharply from ours, he argues, and “dignity” might have been popular for non-moral reasons. So any conclusions we draw by projecting current associations backward are suspect.
Of course, none of this is news to statisticians and linguists. Data and interpretation are their bread and butter. What’s different about Google Ngrams, though, is the temptation to let the sheer volume of data blind us to the ways we can be misled.
This temptation isn’t unique to Ngrams studies; similar errors undermine all sorts of big data projects. Consider, for instance, the case of Google Flu Trends (GFT). Released in 2008, GFT would count words like “fever” and “cough” in millions of Google search queries, using them to “nowcast” how many people had the flu. With those estimates, public health officials could act two weeks before the Centers for Disease Control could calculate the true numbers from doctors’ reports.
When big data isn’t seen as a panacea, it can be transformative.
Initially, GFT was claimed to be 97 percent accurate. But as a study out of Northeastern University documents, that accuracy was a fluke. First, GFT completely missed the “swine flu” pandemic in the spring and summer of 2009. (It turned out that GFT was largely predicting winter.) Then, the system began to overestimate flu cases. In fact, it overshot the peak 2013 numbers by a whopping 140 percent. Eventually, Google just retired the program altogether.
So what went wrong? As with Ngrams, people didn’t carefully consider the sources and interpretation of their data. The data source, Google searches, was not a static beast. When Google started auto-completing queries, users started just accepting the suggested keywords, distorting the searches GFT saw. On the interpretation side, GFT’s engineers initially let GFT take the data at face value; almost any search term was treated as a potential flu indicator. With millions of search terms, GFT was practically guaranteed to over-interpret seasonal words like “snow” as evidence of flu.
But when big data isn’t seen as a panacea, it can be transformative. Several groups, like Columbia University researcher Jeffrey Shaman’s, for example, have outperformed the flu predictions of both the CDC and GFT by using the former to compensate for the skew of the latter. “Shaman’s team tested their model against actual flu activity that had already occurred during the season,” according to the CDC. By taking the immediate past into consideration, Shaman and his team fine-tuned their mathematical model to better predict the future. All it takes is for teams to critically assess their assumptions about their data….(More)
Secret Admirers: An Empirical Examination of Information Hiding and Contribution Dynamics in Online Crowdfunding
Gordon Burtch et al: “Individuals’ actions in online social contexts are growing increasingly visible and traceable. Many online platforms account for this by providing users with granular control over when and how their identity or actions are made visible to peers. However, little work has sought to understand the effect that a user’s decision to conceal information might have on observing peers, who are likely to refer to that information when deciding on their own actions. We leverage a unique impression-level dataset from one of the world’s largest online crowdfunding platforms, where contributors are given the option to conceal their username or contribution amount from public display, with each transaction. We demonstrate that when campaign contributors elect to conceal information, it has a negative influence on subsequent visitors’ likelihood of conversion, as well as on their average contributions, conditional on conversion. Moreover, we argue that social norms are an important driver of information concealment, providing evidence of peer influence in the decision to conceal. We discuss the implications of our results for the provision of online information hiding mechanisms, as well as the design of crowdfunding platforms and electronic markets more generally….(More)”