Peer review in 2015: A global view


A white paper by Taylor & Francis: “Within the academic community, peer review is widely recognized as being at the heart of scholarly research. However, faith in peer review’s integrity is of ongoing and increasing concern to many. It is imperative that publishers (and academic editors) of peer-reviewed scholarly research learn from each other, working together to improve practices in areas such as ethical issues, training, and data transparency….Key findings:

  • Authors, editors and reviewers all agreed that the most important motivation to publish in peer reviewed journals is making a contribution to the field and sharing research with others.
  • Playing a part in the academic process and improving papers are the most important motivations for reviewers. Similarly, 90% of SAS study respondents said that playing a role in the academic community was a motivation to review.
  • Most researchers, across the humanities and social sciences (HSS) and science, technology and medicine (STM), rate the benefit of the peer review process towards improving their article as 8 or above out of 10. This was found to be the most important aspect of peer review in both the ideal and the real world, echoing the earlier large-scale peer review studies.
  • In an ideal world, there is agreement that peer review should detect plagiarism (with mean ratings of 7.1 for HSS and 7.5 for STM out of 10), but agreement that peer review is currently achieving this in the real world is only 5.7 HSS / 6.3 STM out of 10.
  • Researchers thought there was a low prevalence of gender bias but higher prevalence of regional and seniority bias – and suggest that double blind peer review is most capable of preventing reviewer discrimination where it is based on an author’s identity.
  • Most researchers wait between one and six months for an article they’ve written to undergo peer review, yet authors (not reviewers / editors) think up to two months is reasonable .
  • HSS authors say they are kept less well informed than STM authors about the progress of their article through peer review….(More)”

The Transformation of Human Rights Fact-Finding


Book edited by Philip Alston and Sarah Knuckey: “Fact-finding is at the heart of human rights advocacy, and is often at the center of international controversies about alleged government abuses. In recent years, human rights fact-finding has greatly proliferated and become more sophisticated and complex, while also being subjected to stronger scrutiny from governments. Nevertheless, despite the prominence of fact-finding, it remains strikingly under-studied and under-theorized. Too little has been done to bring forth the assumptions, methodologies, and techniques of this rapidly developing field, or to open human rights fact-finding to critical and constructive scrutiny.

The Transformation of Human Rights Fact-Finding offers a multidisciplinary approach to the study of fact-finding with rigorous and critical analysis of the field of practice, while providing a range of accounts of what actually happens. It deepens the study and practice of human rights investigations, and fosters fact-finding as a discretely studied topic, while mapping crucial transformations in the field. The contributions to this book are the result of a major international conference organized by New York University Law School’s Center for Human Rights and Global Justice. Engaging the expertise and experience of the editors and contributing authors, it offers a broad approach encompassing contemporary issues and analysis across the human rights spectrum in law, international relations, and critical theory. This book addresses the major areas of human rights fact-finding such as victim and witness issues; fact-finding for advocacy, enforcement, and litigation; the role of interdisciplinary expertise and methodologies; crowd sourcing, social media, and big data; and international guidelines for fact-finding….(More)”

Privacy in a Digital, Networked World: Technologies, Implications and Solutions


Book edited by Zeadally, Sherali and Badra, Mohamad: “This comprehensive textbook/reference presents a focused review of the state of the art in privacy research, encompassing a range of diverse topics. The first book of its kind designed specifically to cater to courses on privacy, this authoritative volume provides technical, legal, and ethical perspectives on privacy issues from a global selection of renowned experts. Features: examines privacy issues relating to databases, P2P networks, big data technologies, social networks, and digital information networks; describes the challenges of addressing privacy concerns in various areas; reviews topics of privacy in electronic health systems, smart grid technology, vehicular ad-hoc networks, mobile devices, location-based systems, and crowdsourcing platforms; investigates approaches for protecting privacy in cloud applications; discusses the regulation of personal information disclosure and the privacy of individuals; presents the tools and the evidence to better understand consumers’ privacy behaviors….(More)”

Technology is a new kind of lifeline for refugees


Marketplace: “Imagine you’re a refugee leaving home for good. You’ll need help. But what you ask for today is much different than it would have been just 10 years ago.

“What people are demanding, more and more, is not classic food, shelter, water, healthcare, but they demand wifi,” said Melita Šunjić, a spokesperson for the United Nations High Commissioner for Refugees.

Šunjić began her work with Syrian refugees in camps in Amman, Jordan. Many were from rural areas with basic cell phones.

“The refugees we’re looking at now, who are coming to Europe – this is a completely different story,” Šunjić said. “They are middle class, urban people. Practically each family has at least one smart phone. We calculated that in each group of 20, they would have three smart phones.”

Refugees use their phones to call home and to map their routes. Even smugglers have their own Facebook pages.

“I don’t remember a crisis or refugee group where modern technology played such a role,” Šunjić said.

As refugees from Syria continue to flow into Europe, aid organizations are gearing up for what promises to be a difficult winter.

Emily Eros, ‎a GIS mapping officer with the American Red Cross, said her organization is working on the basics like providing food, water and shelter, but it’s also helping refugees stay connected. “It’s a little bit difficult because it’s not just a matter of getting a wifi station up, it’s also a matter of having someone there who’s able to fix it if something goes wrong,” she said. …(More)”

Government as a Platform: a historical and architectural analysis


Paper by Bendik Bygstad and Francis D’Silva: “A national administration is dependent on its archives and registers, for many purposes, such as tax collection, enforcement of law, economic governance, and welfare services. Today, these services are based on large digital infrastructures, which grow organically in volume and scope. Building on a critical realist approach we investigate a particularly successful infrastructure in Norway called Altinn, and ask: what are the evolutionary mechanisms for a successful “government as a platform”? We frame our study with two perspectives; a historical institutional perspective that traces the roots of Altinn back to the Middle Ages, and an architectural perspective that allows for a more detailed analysis of the consequences of digitalization and the role of platforms. We offer two insights from our study: we identify three evolutionary mechanisms of national registers, and we discuss a future scenario of government platforms as “digital commons”…(More)”

Robots Will Make Leeds the First Self-Repairing City


Emiko Jozuka at Motherboard: “Researchers in Britain want to make the first “self-repairing” city by 2035. How will they do this? By creating autonomous repair robots that patrol the streets and drainage systems, making sure your car doesn’t dip into a pothole, and that you don’t experience any gas leaks.

“The idea is to create a city that behaves almost like a living organism,” said Raul Fuentes, a researcher at the School of Civil Engineering at Leeds University, who is working on the project. “The robots will act like white cells that are able to identify bacteria or viruses and attack them. It’s kind of like an immune system.”

The £4.2 million ($6.4 million) national infrastructure project is in collaboration with Leeds City Council and the UK Collaboration for Research in Infrastructures and Cities (UKCRIC). The aim is to create a fleet of robot repair workers who will live in Leeds city, spot problems, and sort them out before they become even bigger ones by 2035, said Fuentes. The project is set to launch officially in January 2016, he added.

For their five-year project—which has a vision that extends until 2050—the researchers will develop robot designs and technologies that focus on three main areas. The first is to create drones that can perch on high structures and repair things like street lamps; the second is to develop drones that can autonomously spot when a pothole is about to form and zone in and patch that up before it worsens; and the third is to develop robots that will live in utility pipes so they can inspect, repair, and report back to humans when they spot an issue.

“The robots will be living permanently in the city, and they’ll be able to identify issues before they become real problems,” explained Fuentes. The researchers are working on making the robots autonomous, and want them to be living in swarms or packs where they can communicate with one another on how best they could get the repair job done….(More)

New flu tracker uses Google search data better than Google


 at ArsTechnica: “With big data comes big noise. Google learned this lesson the hard way with its now kaput Google Flu Trends. The online tracker, which used Internet search data to predict real-life flu outbreaks, emerged amid fanfare in 2008. Then it met a quiet death this August after repeatedly coughing up bad estimates.

But big Internet data isn’t out of the disease tracking scene yet.

With hubris firmly in check, a team of Harvard researchers have come up with a way to tame the unruly data, combine it with other data sets, and continually calibrate it to track flu outbreaks with less error. Their new model, published Monday in the Proceedings of the National Academy of Sciences, out-performs Google Flu Trends and other models with at least double the accuracy. If the model holds up in coming flu seasons, it could reinstate some optimism in using big data to monitor disease and herald a wave of more accurate second-generation models.

Big data has a lot of potential, Samuel Kou, a statistics professor at Harvard University and coauthor on the new study, told Ars. It’s just a question of using the right analytics, he said.

Kou and his colleagues built on Google’s flu tracking model for their new version, called ARGO (AutoRegression with GOogle search data). Google Flu Trends basically relied on trends in Internet search terms, such as headache and chills, to estimate the number of flu cases. Those search terms were correlated with flu outbreak data collected by the Centers for Disease Control and Prevention. The CDC’s data relies on clinical reports from around the country. But compiling and analyzing that data can be slow, leading to a lag time of one to three weeks. The Google data, on the other hand, offered near real-time tracking for health experts to manage and prepare for outbreaks.

At first Google’s tracker appeared to be pretty good, matching CDC data’s late-breaking data somewhat closely. But, two notable stumbles led to its ultimate downfall: an underestimate of the 2009 H1N1 swine flu outbreak and an alarming overestimate (almost double real numbers) of the 2012-2013 flu season’s cases…..For ARGO, he and colleagues took the trend data and then designed a model that could self-correct for changes in how people search. The model has a two-year sliding window in which it re-calibrates current search term trends with the CDC’s historical flu data (the gold standard for flu data). They also made sure to exclude winter search terms, such as March Madness and the Oscars, so they didn’t get accidentally correlated with seasonal flu trends. Last, they incorporated data on the historical seasonality of flu.

The result was a model that significantly out-competed the Google Flu Trends estimates for the period between March 29, 2009 to July 11, 2015. ARGO also beat out other models, including one based on current and historical CDC data….(More)”

See also Proceedings of the National Academy of Sciences, 2015. DOI: 10.1073/pnas.1515373112

Politics and the New Machine


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)”

 

Predictive policing is ‘technological racism’


Shaun King at the New York Daily News: “The future is here.

For years now, the NYPD, the Miami PD, and many police departments around the country have been using new technology that claims it can predict where crime will happen and where police should focus their energies in order. They call it predictive policing. Months ago, I raised several red flags to such software because it does not appear to properly account for the presence of racism or racial profiling in how it predicts where crimes will be committed.

See, these systems claim to predict where crimes will happen based on prior arrest data. What they don’t account for is the widespread reality that race and racial profiling have everything to do with who is arrested and where they are arrested. For instance, study after study has shown that white people actually are more likely to sell drugs and do drugs than black people, but are exponentially less likely to be arrested for either crime. But, and this is where these systems fail, if the only data being entered into systems is based not on the more complex reality of who sells and purchases drugs, but on a racial stereotype, then the system will only perpetuate the racism that preceded it…

In essence, it’s not predicting who will sell drugs and where they will sell it, as much as it is actually predicting where a certain race of people may sell or purchase drugs. It’s technological racism at its finest.

Now, in addition to predictive policing, the state of Pennsylvania is pioneering predictive prison sentencing. Through complex questionnaires and surveys completed not by inmates, but by prison staff members, inmates may be given a smaller bail or shorter sentences or a higher bail and lengthier prison sentences. The surveys focus on family background, economic background, prior crimes, education levels and more.

When all of the data is scored, the result classifies prisoners as low, medium or high risk. While this may sound benign, it isn’t. No prisoner should ever be given a harsh sentence or an outrageous bail amount because of their family background or economic status. Even these surveys lend themselves to being racist and putting black and brown women and men in positions where it’s nearly impossible to get a good score because of prevalent problems in communities of color….(More)”

How Satellite Data and Artificial Intelligence could help us understand poverty better


Maya Craig at Fast Company: “Governments and development organizations currently measure poverty levels by conducting door-to-door surveys. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth.

The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide. A recent study conducted by the organization found that more than 50 countries lack legitimate poverty estimates, which limits the ability of the development community to support the world’s poorest populations.

“Data depravation is a serious issue, especially in many of the countries where we need it most,” says David Newhouse, senior economist at the World Bank. “This technology has the potential to help us get that data more frequently and at a finer level of detail than is currently possible.”

The announcement is the latest in an emerging industry of AI analysis of satellite photos. A growing number of investors and entrepreneurs are betting that the convergence of these fields will have far-reaching impacts on business, policy, resource management and disaster response.

Wall Street’s biggest hedge-fund businesses have begun using the technology to improve investment strategies. The Pew Charitable Trust employs the method to monitor oceans for illegal fishing activities. And startups like San Francisco-based Mavrx use similar analytics to optimize crop harvest.

The commercial earth-imaging satellite market, valued at $2.7 billion in 2014, is predicted to grow by 14% each year through the decade, according to a recent report.

As recently as two years ago, there were just four commercial earth imaging satellites operated in the U.S., and government contracts accounted for about 70% of imagery sales. By 2020, there will be hundreds of private-sector “smallsats” in orbit capturing imagery that will be easily accessible online. Companies like Skybox Imaging and Planet Labs have the first of these smallsats already active, with plans for more.

The images generated by these companies will be among the world’s largest data sets. And recent breakthroughs in AI research have made it possible to analyze these images to inform decision-making…(More)”