Sector: science

Politics and the New Machine

Curated on Posted on by Stefaan Verhulst

Jill Lepore in the NewYorker on “What the turn from polls to data science means for democracy”: “…The modern public-opinion poll has been around since the Great Depression, when the response rate—the number of people who take a survey as a percentage of those who were asked—was more than ninety. The participation rate—the number of people who take a survey as a percentage of the population—is far lower. Election pollsters sample only a minuscule portion of the electorate, not uncommonly something on the order of a couple of thousand people out of the more than two hundred million Americans who are eligible to vote. The promise of this work is that the sample is exquisitely representative. But the lower the response rate the harder and more expensive it becomes to realize that promise, which requires both calling many more people and trying to correct for “non-response bias” by giving greater weight to the answers of people from demographic groups that are less likely to respond. Pollster.com’s Mark Blumenthal has recalled how, in the nineteen-eighties, when the response rate at the firm where he was working had fallen to about sixty per cent, people in his office said, “What will happen when it’s only twenty? We won’t be able to be in business!” A typical response rate is now in the single digits.

Meanwhile, polls are wielding greater influence over American elections than ever….

Still, data science can’t solve the biggest problem with polling, because that problem is neither methodological nor technological. It’s political. Pollsters rose to prominence by claiming that measuring public opinion is good for democracy. But what if it’s bad?

A “poll” used to mean the top of your head. Ophelia says of Polonius, “His beard as white as snow: All flaxen was his poll.” When voting involved assembling (all in favor of Smith stand here, all in favor of Jones over there), counting votes required counting heads; that is, counting polls. Eventually, a “poll” came to mean the count itself. By the nineteenth century, to vote was to go “to the polls,” where, more and more, voting was done on paper. Ballots were often printed in newspapers: you’d cut one out and bring it with you. With the turn to the secret ballot, beginning in the eighteen-eighties, the government began supplying the ballots, but newspapers kept printing them; they’d use them to conduct their own polls, called “straw polls.” Before the election, you’d cut out your ballot and mail it to the newspaper, which would make a prediction. Political parties conducted straw polls, too. That’s one of the ways the political machine worked….

Ever since Gallup, two things have been called polls: surveys of opinions and forecasts of election results. (Plenty of other surveys, of course, don’t measure opinions but instead concern status and behavior: Do you own a house? Have you seen a doctor in the past month?) It’s not a bad idea to reserve the term “polls” for the kind meant to produce election forecasts. When Gallup started out, he was skeptical about using a survey to forecast an election: “Such a test is by no means perfect, because a preelection survey must not only measure public opinion in respect to candidates but must also predict just what groups of people will actually take the trouble to cast their ballots.” Also, he didn’t think that predicting elections constituted a public good: “While such forecasts provide an interesting and legitimate activity, they probably serve no great social purpose.” Then why do it? Gallup conducted polls only to prove the accuracy of his surveys, there being no other way to demonstrate it. The polls themselves, he thought, were pointless…

If public-opinion polling is the child of a strained marriage between the press and the academy, data science is the child of a rocky marriage between the academy and Silicon Valley. The term “data science” was coined in 1960, one year after the Democratic National Committee hired Simulmatics Corporation, a company founded by Ithiel de Sola Pool, a political scientist from M.I.T., to provide strategic analysis in advance of the upcoming Presidential election. Pool and his team collected punch cards from pollsters who had archived more than sixty polls from the elections of 1952, 1954, 1956, 1958, and 1960, representing more than a hundred thousand interviews, and fed them into a UNIVAC. They then sorted voters into four hundred and eighty possible types (for example, “Eastern, metropolitan, lower-income, white, Catholic, female Democrat”) and sorted issues into fifty-two clusters (for example, foreign aid). Simulmatics’ first task, completed just before the Democratic National Convention, was a study of “the Negro vote in the North.” Its report, which is thought to have influenced the civil-rights paragraphs added to the Party’s platform, concluded that between 1954 and 1956 “a small but significant shift to the Republicans occurred among Northern Negroes, which cost the Democrats about 1 per cent of the total votes in 8 key states.” After the nominating convention, the D.N.C. commissioned Simulmatics to prepare three more reports, including one that involved running simulations about different ways in which Kennedy might discuss his Catholicism….

Data science may well turn out to be as flawed as public-opinion polling. But a stage in the development of any new tool is to imagine that you’ve perfected it, in order to ponder its consequences. I asked Hilton to suppose that there existed a flawless tool for measuring public opinion, accurately and instantly, a tool available to voters and politicians alike. Imagine that you’re a member of Congress, I said, and you’re about to head into the House to vote on an act—let’s call it the Smeadwell-Nutley Act. As you do, you use an app called iThePublic to learn the opinions of your constituents. You oppose Smeadwell-Nutley; your constituents are seventy-nine per cent in favor of it. Your constituents will instantly know how you’ve voted, and many have set up an account with Crowdpac to make automatic campaign donations. If you vote against the proposed legislation, your constituents will stop giving money to your reëlection campaign. If, contrary to your convictions but in line with your iThePublic, you vote for Smeadwell-Nutley, would that be democracy? …(More)”

 

Push, Pull, and Spill: A Transdisciplinary Case Study in Municipal Open Government

Curated on Posted on by Stefaan Verhulst

Paper by Jan Whittington et al: “Cities hold considerable information, including details about the daily lives of residents and employees, maps of critical infrastructure, and records of the officials’ internal deliberations. Cities are beginning to realize that this data has economic and other value: If done wisely, the responsible release of city information can also release greater efficiency and innovation in the public and private sector. New services are cropping up that leverage open city data to great effect.

Meanwhile, activist groups and individual residents are placing increasing pressure on state and local government to be more transparent and accountable, even as others sound an alarm over the privacy issues that inevitably attend greater data promiscuity. This takes the form of political pressure to release more information, as well as increased requests for information under the many public records acts across the country.

The result of these forces is that cities are beginning to open their data as never before. It turns out there is surprisingly little research to date into the important and growing area of municipal open data. This article is among the first sustained, cross-disciplinary assessments of an open municipal government system. We are a team of researchers in law, computer science, information science, and urban studies. We have worked hand-in-hand with the City of Seattle, Washington for the better part of a year to understand its current procedures from each disciplinary perspective. Based on this empirical work, we generate a set of recommendations to help the city manage risk latent in opening its data….(More)”

Remaking Participation: Science, Environment and Emergent Publics

Curated on Posted on by Stefaan Verhulst

Book edited by Jason Chilvers and Matthew Kearnes: “Changing relations between science and democracy – and controversies over issues such as climate change, energy transitions, genetically modified organisms and smart technologies – have led to a rapid rise in new forms of public participation and citizen engagement. While most existing approaches adopt fixed meanings of ‘participation’ and are consumed by questions of method or critiquing the possible limits of democratic engagement, this book offers new insights that rethink public engagements with science, innovation and environmental issues as diverse, emergent and in the making. Bringing together leading scholars on science and democracy, working between science and technology studies, political theory, geography, sociology and anthropology, the volume develops relational and co-productionist approaches to studying and intervening in spaces of participation. New empirical insights into the making, construction, circulation and effects of participation across cultures are illustrated through examples ranging from climate change and energy to nanotechnology and mundane technologies, from institutionalised deliberative processes to citizen-led innovation and activism, and from the global north to global south. This new way of seeing participation in science and democracy opens up alternative paths for reconfiguring and remaking participation in more experimental, reflexive, anticipatory and responsible ways….(More)”

Behavioural Science, Randomized Evaluations and the Transformation Of Public Policy: The Case of the UK Government

Curated on Posted on by Stefaan Verhulst

Chapter by Peter John: “Behaviour change policy conveys powerful image: groups of psychologists and scientists, maybe wearing white coats, messing with the minds of citizens, doing experiments on them without their consent, and seeking to manipulate their behaviours. Huddled in an office in the heart of Whitehall, or maybe working out of a windowless room in the White House, behavioural scientists are redesigning the messages and regulations that governments make, operating very far from the public’s view. The unsuspecting citizen becomes something akin to the subjects of science fiction novels, such as Huxley’s Brave New World or Zamyatin’s We. The emotional response to these developments is to cry out for a more humanistic form of public policy, a more participatory form of governance, and to base public policy on the common sense and good judgements of citizens and their elected representatives.

Of course, such an account is a massive stereotype, but something of this viewpoint has emerged as a backdrop to critical academic work on the use of behavioural science in government in what is described as the rise of the psychological state (Jones et al 2013a b), which might be seen to represent a step-change in use of psychological and other form of behavioural research to design public policies. Such a claim speaks more generally to the use of scientific ideas by government since the eighteenth century, which has been subject to a considerable amount of theoretical work in recent years, drawing on the work of Foucault, and which has developed into explorations of the theory and practice of governmentality (see Jones et al 2013:182-188).

With behaviour change, the ‘central concern has been to critically evaluate the broader ethical concerns of behavioural governance, which includes tracing geo-historical contingencies of knowledge mobilized in the legitimation of the behavior change agenda itself’ (190). This line of work presents a subtle set of arguments and claims that an empirical account, such as that presented in this chapter, cannot⎯nor should⎯challenge. Nonetheless, it is instructive to find out more about the phenomenon under study and to understand how the uses of behavioural ideas and randomized evaluations are limited and structured by the institutions and actors in the political process, which are following political and organizational ends. Of particular interest is the incremental and patchy nature of the diffusion of ideas, and how the use of behavioural sciences meshes with existing standard operating procedures and routines of bureaucracies. That said, behavioural sciences can make progress within the fragmented and decentralized policy process, and has the power to create innovations in public policies, often helped by articulate advocates of such measures.

The path of ideas in public policy is usually slow, one of gradual diffusion and small changes in operating assumptions, and this route is likely for the use of behavioural sciences. The implication of this line of argument is that agency as well as structure plays an important role in the adoption and diffusion of the ideas from the behavioural sciences. It implies a more limited and less uniform use of ideas and evidence than implied by the critical writers in this field, but one where public argument and debate play a central role….(More)”

Statistical objectivity is a cloak spun from political yarn

Curated on Posted on by Stefaan Verhulst

Angus Deaton at the Financial Times: “The word data means things that are “given”: baseline truths, not things that are manufactured, invented, tailored or spun. Especially not by politics or politicians. Yet this absolutist view can be a poor guide to using the numbers well. Statistics are far from politics-free; indeed, politics is encoded in their genes. This is ultimately a good thing.

We like to deal with facts, not factoids. We are scandalised when politicians try to censor numbers or twist them, and most statistical offices have protocols designed to prevent such abuse. Headline statistics often seem simple but typically have many moving parts. A clock has two hands and 12 numerals yet underneath there may be thousands of springs, cogs and wheels. Politics is not only about telling the time, or whether the clock is slow or fast, but also about how to design the cogs and wheels. Down in the works, even where the decisions are delegated to bureaucrats and statisticians, there is room for politics to masquerade as science. A veneer of apolitical objectivity can be an effective disguise for a political programme.

Just occasionally, however, the mask drops and the design of the cogs and wheels moves into the glare of frontline politics. Consumer price indexes are leading examples of this. Britain’s first consumer price index was based on spending patterns from 1904. Long before the second world war, these weights were grotesquely outdated. During the war, the cabinet was worried about a wage-price spiral and the Treasury committed to hold the now-irrelevant index below the astonishingly precise value of 201.5 (1914=100) through a policy of food subsidies. It would, for example, respond to an increase in the price of eggs by lowering the price of sugar. Reform of the index would have jeopardised the government’s ability to control it and was too politically risky. The index was not updated until 1947….

These examples show the role of politics needs to be understood, and built in to any careful interpretation of the data. We must always work from multiple sources, and look deep into the cogs and wheels. James Scott, the political scientist, noted that statistics are how the state sees. The state decides what it needs to see and how to see it. That politics infuses every part of this is a testament to the importance of the numbers; lives depend on what they show.

For global poverty or hunger statistics, there is no state and no one’s material wellbeing depends on them. Politicians are less likely to interfere with such data, but this also removes a vital source of monitoring and accountability. Politics is a danger to good data; but without politics data are unlikely to be good, or at least not for long….(More)”

 

Interdisciplinary Perspectives on Trust

Curated on Posted on by Stefaan Verhulst

Book edited by Shockley, E., Neal, T.M.S., PytlikZillig, L.M., and Bornstein, B.H.:  “This timely collection explores trust research from many angles while ably demonstrating the potential of cross-discipline collaboration to deepen our understanding of institutional trust. Citing, among other things, current breakdowns of trust in prominent institutions, the book presents a multilevel model identifying universal aspects of trust as well as domain- and context-specific variations deserving further study. Contributors analyze similarities and differences in trust across public domains from politics and policing to medicine and science, and across languages and nations. Innovative strategies for measuring and assessing trust also shed new light on this essentially human behavior.

Highlights of the coverage:

  • Consensus on conceptualizations and definitions of trust: are we there yet?
  • Differentiating between trust and legitimacy in public attitudes towards legal authority.
  • Examining the relationship between interpersonal and institutional trust in political and health care contexts.
  • Trust as a multilevel phenomenon across contexts.
  • Institutional trust across cultures.
  • The “dark side” of institutional trust….(more)”

When Lobbyists Write Legislation, This Data Mining Tool Traces The Paper Trail

Curated on Posted on by Stefaan Verhulst

FastCoExist: “Most kids learn the grade school civics lesson about how a bill becomes a law. What those lessons usually neglect to show is how legislation today is often birthed on a lobbyist’s desk.

But even for expert researchers, journalists, and government transparency groups, tracing a bill’s lineage isn’t easy—especially at the state level. Last year alone, there were 70,000 state bills introduced in 50 states. It would take one person five weeks to even read them all. Groups that do track state legislation usually focus narrowly on a single topic, such as abortion, or perhaps a single lobby groups.

Computers can do much better. A prototype tool, presented in September at Bloomberg’sData for Good Exchange 2015 conference, mines the Sunlight Foundation’s database of more than 500,000 bills and 200,000 resolutions for the 50 states from 2007 to 2015. It also compares them to 1,500 pieces of “model legislation” written by a few lobbying groups that made their work available, such as the conservative group ALEC (American Legislative Exchange Council) and the liberal group the State Innovation Exchange(formerly called ALICE).

The results are interesting. In one example of the program in use, the team—all from the Data Science for Social Good fellowship program in Chicago—created a graphic (above) that presents the relative influence of ALEC and ALICE in different states. The thickness of each line in the graphic correlates to the percentage of bills introduced in each state that are modeled on either group’s legislation. So a relatively liberal state like New York is mostly ALICE bills, while a “swing” state like Illinois has a lot from both groups….

Along with researchers from the University of Chicago, Wikimedia Foundation, Microsoft Research, and Northwestern University, Walsh is also co-author of another paperpresented at the Bloomberg conference shows how data science can increase government transparency.

Walsh and these co-authors developed software that automatically identifies earmarks in U.S. Congressional bills, showing how representatives are benefiting their own states with pork barrel projects. They verified that it works by comparing it to the results of a massive effort from the U.S. Office of Management and Budget to analyze earmarks for a few limited years. Their results, extended back to 1995 in a public database, showed that there may be many more earmarks than anyone thought.

“Governments are making more data available. It’s something like a needle in a haystack problem, trying to extract all that information out,” says Walsh. “Both of these projects are really about shining light to these dark places where we don’t know what’s going on.”

The state legislation tracker data is available for download here, and the team is working on an expanded system that automatically downloads new state legislation so it can stay up to date…(More)”

Science is best when the data is an open book

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “It was 1986, and the American space agency, NASA, was reeling from the loss of seven lives. The space shuttle Challenger had broken apart about one minute after its launch.

A Congressional commission was formed to report on the tragedy. The physicist Richard Feynman was one of its members.

NASA officials had testified to Congress that the chance of a shuttle failure was around 1 in 100,000. Feynman wanted to look beyond the official testimony to the numbers and data that backed it up.

After completing his investigation, Feynman summed up his findings in an appendix to the Commission’s official report, in which he declaredthat NASA officials had “fooled themselves” into thinking that the shuttle was safe.

After a launch, shuttle parts sometimes came back damaged or behaved in unexpected ways. In many of those cases, NASA came up with convenient explanations that minimised the importance of these red flags. The people at NASA badly wanted the shuttle to be safe, and this coloured their reasoning.

To Feynman, this sort of behaviour was not surprising. In his career as a physicist, Feynman had observed that not just engineers and managers, but also basic scientists have biases that can lead to self-deception.

Feynman believed that scientists should constantly remind themselves of their biases. “The first principle” of being a good researcher, according to Feynman, “is that you must not fool yourself, and you are the easiest person to fool”….In the official report to Congress, Feynman and his colleagues recommended an independent oversight group be established to provide a continuing analysis of risk that was less biased than could be provided by NASA itself. The agency needed input from people who didn’t have a stake in the shuttle being safe.

Individual scientists also need that kind of input. The system of science ought to be set up in such a way that researchers subscribing to different theories can give independent interpretations of the same data set.

This would help protect the scientific community from the tendency for individuals to fool themselves into seeing support for their theory that isn’t there.

To me it’s clear: researchers should routinely examine others’ raw data. But in many fields today there is no opportunity to do so.

Scientists communicate their findings to each other via journal articles. These articles provide summaries of the data, often with a good deal of detail, but in many fields the raw numbers aren’t shared. And the summaries can be artfully arranged to conceal contradictions and maximise the apparent support for the author’s theory.

Occasionally, an article is true to the data behind it, showing the warts and all. But we shouldn’t count on it. As the chemist Matthew Todd has said to me, that would be like expecting a real estate agent’s brochure for a property to show the property’s flaws. You wouldn’t buy a house without seeing it with your own eyes. It can be unwise to buy into a theory without seeing the unfiltered data.

Many scientific societies recognise this. For many years now, some of the journals they oversee have had a policy of requiring authors to provide the raw data when other researchers request it.

Unfortunately, this policy has failed spectacularly, at least in some areas of science. Studies have found that when one researcher requests the data behind an article, that article’s authors respond with the data in fewer than half of cases. This is a major deficiency in the system of science, an embarrassment really.

The well-intentioned policy of requiring that data be provided upon request has turned out to be a formula for unanswered emails, for excuses, and for delays. A data before request policy, however, can be effective.

A few journals have implemented this, requiring that data be posted online upon publication of the article…(More)”

Advancing Open and Citizen-Centered Government

Curated on Posted on by Stefaan Verhulst

The White House: “Today, the United States released our third Open Government National Action Plan, announcing more than 40 new or expanded initiatives to advance the President’s commitment to an open and citizen-centered government….In the third Open Government National Action Plan, the Administration both broadens and deepens efforts to help government become more open and more citizen-centered. The plan includes new and impactful steps the Administration is taking to openly and collaboratively deliver government services and to support open government efforts across the country. These efforts prioritize a citizen-centric approach to government, including improved access to publicly available data to provide everyday Americans with the knowledge and tools necessary to make informed decisions.

One example is the College Scorecard, which shares data through application programming interfaces (APIs) to help students and families make informed choices about education. Open APIs help create an ecosystem around government data in which civil society can provide useful visual tools, making this data more accessible and commercial developers can enable even more value to be extracted to further empower students and their families. In addition to these newer approaches, the plan also highlights significant longstanding open government priorities such as access to information, fiscal transparency, and records management, and continues to push for greater progress in that work.

The plan also focuses on supporting implementation of the landmark 2030 Agenda for Sustainable Development, which sets out a vision and priorities for global development over the next 15 years and was adopted last month by 193 world leaders including President Obama. The plan includes commitments to harness open government and progress toward the Sustainable Development Goals (SDGs) both in the United States and globally, including in the areas of education, health, food security, climate resilience, science and innovation, justice and law enforcement. It also includes a commitment to take stock of existing U.S. government data that relates to the 17 SDGs, and to creating and using data to support progress toward the SDGs.

Some examples of open government efforts newly included in the plan:

  • Promoting employment by unlocking workforce data, including training, skill, job, and wage listings.
  • Enhancing transparency and participation by expanding available Federal services to theOpen311 platform currently available to cities, giving the public a seamless way to report problems and request assistance.
  • Releasing public information from the electronically filed tax forms of nonprofit and charitable organizations (990 forms) as open, machine-readable data.
  • Expanding access to justice through the White House Legal Aid Interagency Roundtable.
  • Promoting open and accountable implementation of the Sustainable Development Goals….(More)”

Anyone can help with crowdsourcing future antibiotics

Curated on Posted on by Stefaan Verhulst

Springwise: “We’ve seen examples of researchers utilizing crowdsourcing to expand their datasets, such as a free mobile app where users help find data patterns in cancer research by playing games. Now a pop-up home lab is harnessing the power of citizen scientists to find future antibiotics in their backyards.
By developing a small home lab, UK-based Post/Biotics is encouraging anyone, including school children, to help find solutions to the growing antibiotics resistance crisis. Post/Biotics is a citizen’s science platform, which provides the toolkit, knowledge and science network so anyone can support antibiotic development. Participants can test samples of basically anything they find in natural areas, from soil to mushrooms, and if their sample has antibacterial properties, their tool will change color. They can then send results, along with a photo and GPS location to an online database. When the database notices a submission that may be interesting, it alerts researchers, who can then ask for samples. An open-source library of potential antimicrobials is then established, and users simultaneously benefit from learning how to conduct microbiology experiments.
Post/Biotics are using the power of an unlimited amount of citizen scientists to increase the research potential of antibiotic discovery….(More)”

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