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The right-wing ideologues were sorely disappointed, and reacted viciously in the political sphere by attacking their own scientist, but Muller’s scientific integrity overcame any biases he might have harbored at the beginning. He “called ‘em as he saw ‘em” and told truth to power.

it speaks well of the scientific process when a prominent skeptic like Muller does his job properly and admits that his original biases were wrong. As reported in the Los Angeles Times : Ken Caldeira, an atmospheric scientist at the Carnegie Institution for Science, which contributed some funding to the Berkeley effort, said Muller’s statement to Congress was “honorable” in recognizing that “previous temperature reconstructions basically got it right…. Willingness to revise views in the face of empirical data is the hallmark of the good scientific process.”

This is the essence of the scientific method at its best. There may be biases in our perceptions, and we may want to find data that fits our preconceptions about the world, but if science is done properly, we get a real answer, often one we did not expect or didn’t want to hear. That’s the true test of when science is giving us a reality check: when it tells us “an inconvenient truth”, something we do not like, but is inescapable if one follows the scientific method and analyzes the data honestly.

Sit down before fact as a little child, be prepared to give up every preconceived notion, follow humbly wherever and to whatever abysses nature leads, or you shall learn nothing.

As for the rise of the PAP, my personal view is that in the late 1950s the PAP was the only viable alternative to colonial rule, once Marshall had bailed - that is, in terms of getting Singapore out of its postwar social and economic predicament. As much as my heart is with the idealists who founded the Barisan, I'm not sure they would have achieved the same practical results as the PAP did in its first 5 years, had they got into power. There were already rifts in the Barisan prior to Operation Cold Store in 1963, and the more one looks into the party at this time, the more chaotic it appears. (Undoubtedly, this chaos was also a result of the pressures exerted upon it by the PAP.)

when the Barisan was systematically destroyed, hopeless though its leaders might have proved as technocrats, Singapore turned a corner. From 1963, economic success and political stability were won at the expense of freedom of expression and 'responsible dissent', generating a conformity, an intellectual sterility and a deep loss of historical identity that I hope the Epilogue to the book conveys. That's basically my take on the rise of the PAP. The party became something very different from 1963.

But in March, climate scientist Raymond Bradley of the University of Massachusetts asked GMU, based in Fairfax, Va., to investigate "clear plagiarism" of one of his textbooks. Bradley says he learned of the copying on the Deep Climate website and through a now year-long analysis of the Wegman report made by retired computer scientist John Mashey of Portola Valley, Calif. Mashey's analysis concludes that 35 of the report's 91 pages "are mostly plagiarized text, but often injected with errors, bias and changes of meaning." Copying others' text or ideas without crediting them violates universities' standards, according to Liz Wager of the London-based Committee on Publication Ethics.

First, we count if we can, but counting depends a great deal on previous assumptions about categorization. Consider, for example, the number of homeless people in Philadelphia, or the number of battered women in Atlanta, or the number of suicides in Denver. Is someone homeless if he’s unemployed and living with his brother’s family temporarily? Do we require that a women self-identify as battered to count her as such? If a person starts drinking day in and day out after a cancer diagnosis and dies from acute cirrhosis, did he kill himself? The answers to such questions significantly affect the count.

Second, after we’ve gathered some numbers relating to a phenomenon, we must reasonably aggregate them into some sort of recommendation or ranking. This is not easy. By appropriate choices of criteria, measurement protocols and weights, almost any desired outcome can be reached.

Are there good reasons the authors picked the criteria they did? Why did they weigh the criteria in the way they did?

Since the answer to the last question is usually yes, the problem of reasonable aggregation is no idle matter.

These two basic procedures — counting and aggregating — have important implications for public policy. Consider the plan to evaluate the progress of New York City public schools inaugurated by the city a few years ago. While several criteria were used, much of a school’s grade was determined by whether students’ performance on standardized state tests showed annual improvement. This approach risked putting too much weight on essentially random fluctuations and induced schools to focus primarily on the topics on the tests. It also meant that the better schools could receive mediocre grades becausethey were already performing well and had little room for improvement. Conversely, poor schools could receive high grades by improving just a bit.

Medical researchers face similar problems when it comes to measuring effectiveness.

Suppose that whenever people contract the disease, they always get it in their mid-60s and live to the age of 75. In the first region, an early screening program detects such people in their 60s. Because these people live to age 75, the five-year survival rate is 100 percent. People in the second region are not screened and thus do not receive their diagnoses until symptoms develop in their early 70s, but they, too, die at 75, so their five-year survival rate is 0 percent. The laissez-faire approach thus yields the same results as the universal screening program, yet if five-year survival were the criterion for effectiveness, universal screening would be deemed the best practice.

Because so many criteria can be used to assess effectiveness — median or mean survival times, side effects, quality of life and the like — there is a case to be made against mandating that doctors follow what seems at any given time to be the best practice. Perhaps, as some have suggested, we should merely nudge them with gentle incentives. A comparable tentativeness may be appropriate when devising criteria for effective schools.

Arrow’s Theorem, a famous result in mathematical economics, essentially states that no voting system satisfying certain minimal conditions can be guaranteed to always yield a fair or reasonable aggregation of the voters’ rankings of several candidates. A squishier analogue for the field of social measurement would say something like this: No method of measuring a societal phenomenon satisfying certain minimal conditions exists that can’t be second-guessed, deconstructed, cheated, rejected or replaced. This doesn’t mean we shouldn’t be counting — but it does mean we should do so with as much care and wisdom as we can muster.

Willer and Feinberg tested participants' belief in global warming, and then their belief in what's called the just-world theory, which holds that life is generally fair and predictable. The subjects were then randomly assigned to read one of two newspaper-style articles. Both pieces were identical through the first four paragraphs, providing basic scientific information about climate change, but they differed in their conclusions, with one article detailing the possibly apocalyptic consequences of climate change, and the other ending with a more upbeat message about potential solutions to global warming.

participants given the doomsday articles came out more skeptical of climate change, while those who read the bright-side pieces came out less skeptical. The increase in skepticism was especially acute among subjects who'd scored high on the just-world scale, perhaps because the worst victims of global warming — the poor of the developing world, future generations, blameless polar bears — are the ones least responsible for it. Such unjust things couldn't possibly occur, and so the predictions can't be true. The results, Willer and Feinberg wrote, "demonstrate how dire messages warning of the severity of global warming and its presumed dangers can backfire ... by contradicting individuals' deeply held beliefs that the world is fundamentally just."

a climate scientist armed with data might argue that worldviews should be trumped by facts. But there's no denying that climate skepticism is on the rise

politicians — mostly on the right — have aggressively pushed the climate-change-is-a-hoax trope. The Climategate controversy of a year ago certainly might have played a role, too, though the steady decline in belief began well before those hacked e-mails were published. Still, the fact remains that if the point of the frightening images in global-warming documentaries like An Inconvenient Truth was to push audiences to act on climate change, they've been a failure theoretically and practically.

I have no doubt whatsoever that Sir Paul Nurse knows more about genetics than I do. It is, after all, where the field in which he won his Nobel prize. As for science, sure, Nurse has the advantage over me there, too. He has a PhD. He’s a science graduate and I’m an arts graduate. But then I’ve never pretended otherwise. My case is not that I “James Delingpole have taken a long hard look at the science of global warming and discovered through careful sifting of countless peer-reviewed papers that the experts have got it all wrong.”

What I am saying, and I say almost every day, is that the evidence is not as robust as the “consensus” scientists claim; that there are many distinguished scientists all round the world who dispute this alleged “consensus”; that true science doesn’t advance through “consensus” and never has; that the Climategate emails threw the peer-review process into serious doubt by demonstrating how eminently corruptable it is; that there are many vested interests out there determined and able to spend a great deal of money by making out that the case for catastrophic, man-made global warming is much stronger than it is. And on these specific issues I can reasonably claim to be better informed than Sir Paul Nurse, regardless of how many PhDs he has, because I’ve spent much more time than he has researching them and because they are not issues which require an exclusively scientific knowledge to understand. They just require the basic journalistic skill of being able to read and analyse.

The aim of the Pall et al paper was to examine a specific event – floods in the UK in Oct/Nov 2000. Normally, with a single event there isn’t enough information to do any attribution, but Pall et al set up a very large ensemble of runs starting from roughly the same initial conditions to see how often the flooding event occurred. Note that flooding was defined as more than just intense rainfall – the authors tracked runoff and streamflow as part of their modelled setup. Then they repeated the same experiments with pre-industrial conditions (less CO2 and cooler temperatures). If the amount of times a flooding event would occur increased in the present-day setup, you can estimate how much more likely the event would have been because of climate change. The results gave varying numbers but in nine out of ten cases the chance increased by more than 20%, and in two out of three cases by more than 90%. This kind of fractional attribution (if an event is 50% more likely with anthropogenic effects, that implies it is 33% attributable) has been applied also to the 2003 European heatwave, and will undoubtedly be applied more often in future. One neat and interesting feature of these experiments was that they used the climateprediction.net set up to harness the power of the public’s idle screensaver time.

The second paper is a more standard detection and attribution study. By looking at the signatures of climate change in precipitation intensity and comparing that to the internal variability and the observation, the researchers conclude that the probability of intense precipitation on any given day has increased by 7 percent over the last 50 years – well outside the bounds of natural variability. This is a result that has been suggested before (i.e. in the IPCC report (Groisman et al, 2005), but this was the first proper attribution study (as far as I know). The signal seen in the data though, while coherent and similar to that seen in the models, was consistently larger, perhaps indicating the models are not sensitive enough, though the El Niño of 1997/8 may have had an outsize effect.

Both papers were submitted in March last year, prior to the 2010 floods in Pakistan, Australia, Brazil or the Philippines, and so did not deal with any of the data or issues associated with those floods. However, while questions of attribution come up whenever something weird happens to the weather, these papers demonstrate clearly that the instant pop-attributions we are always being asked for are just not very sensible. It takes an enormous amount of work to do these kinds of tests, and they just can’t be done instantly. As they are done more often though, we will develop a better sense for the kinds of events that we can say something about, and those we can’t.

There is always concern that the start and end points for any trend study are not appropriate (both sides are guilty on this IMO). I have read precipitation studies were more difficult due to sparse data, and it seems we would have seen precipitation trend graphs a lot more often by now if it was straight forward. 7% seems to be a large change to not have been noted (vocally) earlier, seems like there is more to this story.

Keith Kloor, blogging on the session  at Collide-a-Scape, included a sobering assessment of the scientist-journalist tensions over global warming from Tom Rosensteil, a panelist and long-time journalist who now heads up Pew’s Project for Excellence in Journalism: If you’re waiting for the press to persuade the public, you’re going to lose. The press doesn’t see that as its job.

scientists have  a great opportunity, and responsibility, to tell their own story more directly, as some are doing occasionally through Dot Earth “ Post Cards” and The Times’ Scientist at Work blog.

Naomi Oreskes, a political scientist at the University of California, San Diego, and co-author of “Merchants of Doubt“: Of Mavericks and Mules Gavin Schmidt of NASA’s Goddard Institute for Space Studies and Realclimate.org: Between Sound Bites and the Scientific Paper: Communicating in the Hinterland Thomas Lessl, a scholar at the University of Georgia focused on the cultural history of science: Reforming Scientific Communication About Anthropogenic Climate Change

I focused on two words in the title of the session — diversity and denial. The diversity of lines of inquiry in climate science has a two-pronged impact. It helps build a robust overall picture of a growing human influence on a complex system. But for many of the most important  pixel points in that picture, there is robust, durable and un-manufactured debate. That debate can then be exploited by naysayers eager to cast doubt on the enterprise, when in fact — as I’ve written here before — it’s simply the (sometimes ugly) way that science progresses.

My denial, I said, lay in my longstanding presumption, like that of many scientists and journalists, that better communication of information will tend to change people’s perceptions, priorities and behavior. This attitude, in my view, crested for climate scientists in the wake of the 2007 report from the Intergovernmental Panel on Climate Change.

In his talk, Thomas Lessl said much of this attitude is rooted in what he and some other social science scholars call “scientism,” the idea — rooted in the 19th century — that scientific inquiry is a “distinctive mode of inquiry that promises to bring clarity to all human endeavors.” [5:45 p.m. | Updated Chris Mooney sent an e-mail noting how the discussion below resonates with "Do Scientists Understand the Public," a report he wrote last year for the American Academy of Arts and Sciences and explored here.]

Scientism, though it is good at promoting the recognition that scientific knowledge is the only kind of knowledge, also promotes communication behavior that is bad for the scientific ethos. By this I mean that it turns such communication into combat. By presuming that scientific understanding is the only criterion that matters, scientism inclines public actors to treat resistant audiences as an enemy: If the public doesn’t get the science, shame on the public. If the public rejects a scientific claim, it is either because they don’t get it or because they operate upon some sinister motive.

Scientific knowledge cannot take the place of prudence in public affairs.

Prudence, according to Robert Harriman, “is the mode of reasoning about contingent matters in order to select the best course of action. Contingent events cannot be known with certainty, and actions are intelligible only with regard to some idea of what is good.”

Scientism tends to suppose a one-size-fits-all notion of truth telling. But in the public sphere, people don’t think that way. They bring to the table a variety of truth standards: moral judgment, common-sense judgment, a variety of metaphysical perspectives, and ideological frameworks. The scientists who communicate about climate change may regard these standards as wrong-headed or at best irrelevant, but scientists don’t get to decide this in a democratic debate. When scientists become public actors, they have stepped outside of science, and they are obliged to honor the rules of communication and thought that govern the rest of the world. This might be different, if climate change was just about determining the causes of climate change, but it never is. Getting from the acceptance of ACC to acceptance of the kinds of emissions-reducing policies that are being advocated takes us from one domain of knowing into another.

One might object by saying that the formation of public policy depends upon first establishing the scientific bases of ACC, and that the first question can be considered independently of the second. Of course that is right, but that is an abstract academic distinction that does not hold in public debates. In public debates a different set of norms and assumptions apply: motive is not to be casually set aside as a nonfactor. Just because scientists customarily bracket off scientific topics from their policy implications does not mean that lay people do this—or even that they should be compelled to do so. When scientists talk about one thing, they seem to imply the other. But which is the motive force? Are they advocating for ACC because they subscribe to a political worldview that supports legal curtailments upon free enterprise? Or do they support such a political worldview because they are convinced of ACC? The fact that they speak as scientists may mean to other scientists that they reason from evidence alone. But the public does not necessarily share this assumption. If scientists don’t respect this fact about their audiences, they are bound to get in trouble. [Read the rest.]

The idea that increased energy efficiency can increase energy consumption at the macro-economic level strikes many as a new idea, or paradoxical, but it was first observed in 1865 by British economist William Stanley Jevons, who pointed out that Watt's more efficient steam engine and other technical improvements that increased the efficiency of coal consumption actually increased rather than decreased demand for coal. More efficient engines, Jevons argued, would increase future coal consumption by lowering the effective price of energy, thus spurring greater demand and opening up useful and profitable new ways to utilize coal. Jevons was proven right, and the reality of what is today known as "Jevons Paradox" has long been uncontroversial among economists.

Economists have long observed that increasing the productivity of any single factor of production -- whether labor, capital, or energy -- increases demand for all of those factors. This is one of the basic dynamics of economic growth. Luddites who feared there would be fewer jobs with the emergence of weaving looms were proved wrong by lower price for woven clothing and demand that has skyrocketed (and continued to increase) ever since. And today, no economist would posit that an X% improvement in labor productivity would lead directly to an X% reduction in employment. In fact, the opposite is widely expected: labor productivity is a chief driver of economic growth and thus increases in employment overall. There is no evidence, the report points out, that energy is any different, as per capita energy consumption everywhere on earth continues to rise, even as economies become more efficient each year.

A hefty new volume published by Wiley-Blackwell and edited by the climate scientists David Archer and Raymond Pierrehumbert at the University of Chicago, it’s a rich feast for anyone who wants to trace the history of climate science from its earliest origins to the present.

Rhys Morgan , a schoolboy with Crohns disease. Last year, chatting on crohnsforum.com, he saw people recommending "Miracle Mineral Solution", which turned out to be industrial bleach, sold with a dreary conspiracy theory to cure Aids, cancer and so on.Aged 15, he was perfectly capable of exploring the evidence, finding official documents , and explaining why it was dangerous. The adults banned him. Since then he's got his story on The One Show, while the chief medical officer for Wales, the Food Standards Agency and Trading Standards have waded in.

If every school taught the basics – randomised trials, blinding, cohort studies, and why systematic reviews are better than cherrypicking your evidence – it would help everyone navigate the world, and learn some of the most important ideas in the whole of science.

We could continuously sample a news source’s output to produce ongoing accuracy estimates, and build social software to help the audience report and filter errors. Meticulously applied, this approach would give a measure of the accuracy of each information source, and a measure of the efficiency of their corrections process (currently only about 3% of all errors are corrected.)

Real world reporting isn’t always clearly “right” or “wrong,” so it will often be hard to decide whether something is an error or not. But we’re not going for ultimate Truth here,  just a general way of measuring some important aspect of the idea we call “accuracy.” In practice it’s important that the error counting method is simple, clear and repeatable, so that you can compare error rates of different times and sources.

Subjective errors, though by definition involving judgment, should not be dismissed as merely differences in opinion. Sources found such errors to be about as common as factual errors and often more egregious [as rated by the sources.] But subjective errors are a very complex category

One of the major problems with previous news accuracy metrics is the effort and time required to produce them. In short, existing accuracy measurement methods are expensive and slow. I’ve been wondering if we can do better, and a simple idea comes to mind: sampling. The core idea is this: news sources could take an ongoing random sample of their output and check it for accuracy — a fact check spot check

Standard statistical theory tells us what the error on that estimate will be for any given number of samples (If I’ve got this right, the relevant formula is standard error of a population proportion estimate without replacement.) At a sample rate of a few stories per day, daily estimates of error rate won’t be worth much. But weekly and monthly aggregates will start to produce useful accuracy estimates

the first step would be admitting how inaccurate journalism has historically been. Then we have to come up with standardized accuracy evaluation procedures, in pursuit of metrics that capture enough of what we mean by “true” to be worth optimizing. Meanwhile, we can ramp up the efficiency of our online corrections processes until we find as many useful, legitimate errors as possible with as little staff time as possible. It might also be possible do data mining on types of errors and types of stories to figure out if there are patterns in how an organization fails to get facts right.

I’d love to live in a world where I could compare the accuracy of information sources, where errors got found and fixed with crowd-sourced ease, and where news organizations weren’t shy about telling me what they did and did not know. Basic factual accuracy is far from the only measure of good journalism, but perhaps it’s an improvement over the current sad state of affairs

Muller's data (black line) tracks pretty well with the three established data sets. This is just an initial sampling of Muller's data—just 2 percent of the 1.6 billion records he's working with—but these early findings are incredibly consistent with the previous findings

In his testimony, Muller made these points (emphasis mine): The Berkeley Earth Surface Temperature project was created to make the best possible estimate of global temperature change using as complete a record of measurements as possible and by applying novel methods for the estimation and elimination of systematic biases. We see a global warming trend that is very similar to that previously reported by the other groups. The world temperature data has sufficient integrity to be used to determine global temperature trends. Despite potential biases in the data, methods of analysis can be used to reduce bias effects well enough to enable us to measure long-term Earth temperature changes. Data integrity is adequate. Based on our initial work at Berkeley Earth, I believe that some of the most worrisome biases are less of a problem than I had previously thought.

For the many climate deniers who hang their arguments on Muller's "doubts," this is a severe blow. Of course, when the hard scientific truths are inconvenient, climate denying House leaders can always call a lawyer, a marketing professor, and an economist into the scientific hearing.

It is difficult to say if the "failure" of the academics was due to an unstinting belief in free markets or conflicts of interest. Parts of the movie did appear to be trying too hard to prove the point. However, the threat posed by academics earning consulting fees that dwarf their academic compensation, and which might therefore impair their independence, is a real one.

One of the worst was the Ivy League university economics professor engaged by the Icelandic Chamber of Commerce to co-author a report on the Icelandic financial system. He concluded that the system was sound even though there were numerous warning signs. When he was asked how he arrived at his conclusions, he said he had talked to people and were misled by them. One wonders how much of his conclusions were actually based on rigorous analysis.

it is troubling if academics merely become mouthpieces for vested interests. The impression one gets from watching the movie certainly does not fit with the high level of trust in academics shown by the Edelman Trust Barometer.

As an academic, I have often been told that I can be independent and objective - that I should have no axe to grind and no wheels to grease. However, I worry about an erosion of trust in academics. This may be especially true in certain disciplines like business (which is mine, incidentally).

too many business school professors were serving on US corporate boards and have lost their willingness to be critical about unethical business practices. In corporate scandals such as Enron and Satyam, academics from top business schools have not particularly covered themselves in glory.

It is more and more common for universities - in the US and here - to invite business people to serve on their boards.

universities and academics may lose their independence and objectivity in commenting on business issues critically, for fear of offending those who ultimately have an oversight role over the varsity's senior management.

Universities might also have business leaders serving on boards as potential donors, which would also confuse the role of board members and lead to conflicts of interest. In the Satyam scandal in India, the founder of Satyam sat on the board of the Indian School of Business, while the Dean of the Indian School of Business sat on Satyam's board. Satyam also made a significant donation to the Indian School of Business.

Universities are increasingly dependent on funding from industry and wealthy individuals as well as other sources, sometimes even dubious ones. The recent scandal at the London School of Economics involving its affiliation with Libya is an example.

It is important for universities to have robust gift policies as part of the risk management to protect their reputation, which can be easily tainted if a donation comes from a questionable source. It is especially important that donations do not cause universities to be captured by vested interests.

From time to time, people in industry ask me if I have been pressured by the university to tone down on my outspokenness on corporate governance issues. Thankfully, while there have been instances where varsity colleagues and friends in industry have conveyed messages from others to "tone down", I have felt relatively free to express my views. Of course, were I trying to earn more money from external consulting, I guess I would be less vocal.

I do worry about the loss of independence and, therefore, trust in academics and academic institutions if we are not careful about it.

So when I hear scientists say, “The data speak for themselves,” I cringe. Data never speak. And data generally and most often are politically and socially inflected. They have import for people’s lives. To ignore that is to ignore the social and cultural dimensions within which this science is taking place.

I do think that there is a process by which, for example, the connection between cigarette smoking and cancer for decades had a scientific consensus that this was an issue, then a social process begins, and then it becomes accepted.

The interesting thing with climate change, I find, is that positioning on climate change is strikingly predictable based on someone’s political leanings. One-third of Republicans and three-quarters of Democrats think that climate change is real. That to me speaks to the political, ideological and cultural dimensions of this debate.

It’s interesting because it wasn’t always so. In 1997 with the Kyoto treaty, with the development of regulations that would impact economic and political interests, sides started to be drawn. We’ve reached the stage today that climate change has become part of the culture wars, the same as health care, abortion, gun control and evolution.

There are many who distrust the peer-review process and distrust scientists. So that can be step one. I think a lot of people will be uncomfortable accepting a scientific conclusion if it necessarily leads to outcomes they find objectionable. People will be hesitant to accept the notion of climate change if that leads directly towards ideas that are at variance with values that they hold dear.

do you trust the scientific process? Do you trust scientists? The faith-and-reason debate has been around for centuries. I just read a book that I thought was prescient, “Anti-Intellectualism in American Life,” about this suspicion people have about intellectuals who are working on issues that are inaccessible, opaque to them, yielding conclusions that alter the way we structure our society, the way we live our lives.

There’s a certain helpless frustration people have: Who are these cultural elites, these intellectual elites who can make these conclusions in the ivory tower of academia or other scientific institutions and tell me how to live my life?

And we can’t leave out power. There are certain powerful interests out there that will not accept the conclusions this will yield to, therefore they will not accept the definition of the problem if they are not going to accept the solutions that follow it. I’m speaking of certain industry sectors that stand to lose in a carbon-constrained world.

Also, if you can’t define solutions on climate change and you’re asking me to accept it, you’re asking me to accept basically a pretty dismal reality that I refuse to accept. And many climate proponents fall into this when they give these horrific, apocalyptic predictions of cities under water and ice ages and things like that. That tends to get people to dig their heels in even harder.

Some people look at this as just a move for more government, more government bureaucracy. And I think importantly fear or resist the idea of world government. Carbon dioxide is part of the economy of every country on earth. This is a global cooperation challenge the likes of which we have never seen before.

Do you trust the message and do you trust the messenger? If I am inclined to resist the notion of global cooperation — which is a nice way to put what others may see as a one-world government — and if the scientific body that came to that conclusion represents that entity, I will be less inclined to believe it. People will accept a message from someone that they think shares their values and beliefs. And for a lot of people, environmentalists are not that kind of person. There’s a segment of the population that sees environmentalists as socialists, trying to control people’s lives.

In our society today, I think people have more faith in economic institutions than they do in scientific institutions. Scientists can talk until they are blue in the face about climate change. But if businesses are paying money to address this issue, then people will say: It must be true, because they wouldn’t be throwing their money away.

what I’m laying out is that this is very much a value- and culture-based debate. And to ignore that – you will never resolve it and you will end up in what I have described a logic schism, where the two sides talk about completely different things, completely different issues, demonizing the other, only looking for things that confirm their opinion. And we get nowhere.

The theory of motivated reasoning builds on a key insight of modern neuroscience (PDF): Reasoning is actually suffused with emotion (or what researchers often call "affect"). Not only are the two inseparable, but our positive or negative feelings about people, things, and ideas arise much more rapidly than our conscious thoughts, in a matter of milliseconds—fast enough to detect with an EEG device, but long before we're aware of it. That shouldn't be surprising: Evolution required us to react very quickly to stimuli in our environment. It's a "basic human survival skill," explains political scientist Arthur Lupia of the University of Michigan. We push threatening information away; we pull friendly information close. We apply fight-or-flight reflexes not only to predators, but to data itself.

We're not driven only by emotions, of course—we also reason, deliberate. But reasoning comes later, works slower—and even then, it doesn't take place in an emotional vacuum. Rather, our quick-fire emotions can set us on a course of thinking that's highly biased, especially on topics we care a great deal about.

Consider a person who has heard about a scientific discovery that deeply challenges her belief in divine creation—a new hominid, say, that confirms our evolutionary origins. What happens next, explains political scientist Charles Taber of Stony Brook University, is a subconscious negative response to the new information—and that response, in turn, guides the type of memories and associations formed in the conscious mind. "They retrieve thoughts that are consistent with their previous beliefs," says Taber, "and that will lead them to build an argument and challenge what they're hearing."

when we think we're reasoning, we may instead be rationalizing. Or to use an analogy offered by University of Virginia psychologist Jonathan Haidt: We may think we're being scientists, but we're actually being lawyers (PDF). Our "reasoning" is a means to a predetermined end—winning our "case"—and is shot through with biases. They include "confirmation bias," in which we give greater heed to evidence and arguments that bolster our beliefs, and "disconfirmation bias," in which we expend disproportionate energy trying to debunk or refute views and arguments that we find uncongenial.

Jeffrey Sachs, an advisor to the United Nations and director of Columbia University's Earth Institute, is one such expert. In books and countless speeches and television appearances, he has argued that poor countries are poor because they are hot, infertile, malaria-infested, and often landlocked; these factors, however, make it hard for them to be productive without an initial large investment to help them deal with such endemic problems. But they cannot pay for the investments precisely because they are poor -- they are in what economists call a "poverty trap." Until something is done about these problems, neither free markets nor democracy will do very much for them.

But then there are others, equally vocal, who believe that all of Sachs's answers are wrong. William Easterly, who battles Sachs from New York University at the other end of Manhattan, has become one of the most influential aid critics in his books, The Elusive Quest for Growth and The White Man's Burden. Dambisa Moyo, an economist who worked at Goldman Sachs and the World Bank, has joined her voice to Easterly's with her recent book, Dead Aid. Both argue that aid does more bad than good. It prevents people from searching for their own solutions, while corrupting and undermining local institutions and creating a self-perpetuating lobby of aid agencies.

The best bet for poor countries, they argue, is to rely on one simple idea: When markets are free and the incentives are right, people can find ways to solve their problems. They do not need handouts from foreigners or their own governments.

According to Easterly, there is no such thing as a poverty trap.

To find out whether there are in fact poverty traps, and, if so, where they are and how to help the poor get out of them, we need to better understand the concrete problems they face. Some aid programs help more than others, but which ones? Finding out required us to step out of the office and look more carefully at the world. In 2003, we founded what became the Abdul Latif Jameel Poverty Action Lab, or J-PAL. A key part of our mission is to research by using randomized control trials -- similar to experiments used in medicine to test the effectiveness of a drug -- to understand what works and what doesn't in the real-world fight against poverty. In practical terms, that meant we'd have to start understanding how the poor really live their lives.

many involved in “open data” saw their interests and activities being confined to making data ‘legally” and “technically” accessible — what happened to it after that was somebody else’s responsibility.

while the Digital Divide deals with, for the most part “infrastructure” issues, the Data Divide is concerned with “content” issues.

where a Digital Divide might exist for example, as a result of geographical or policy considerations and thus have uniform effects on all those on the wrong side of the “divide” whatever their socio-demographic situation; a Data Divide and particularly one of the most significant current components of the Open Data movement i.e. OGD, would have particularly damaging negative effects and result in particularly significant lost opportunities for the most vulnerable groups and individuals in society and globally. (I’ve discussed some examples here at length in a previous blogpost.)

1. infrastructure—being on the wrong side of the “Digital Divide” and thus not having access to the basic infrastructure supporting the availability of OGD. 2. devices—OGD that is not universally accessible and device independent (that only runs on I-Phones for example) 3. software—“accessible” OGD that requires specialized technical software/training to become “usable” 4. content—OGD not designed for use by those with handicaps, non-English speakers, those with low levels of functional literacy for example 5.  interpretation/sense-making—OGD that is only accessible for use through a technical intermediary and/or is useful only if “interpreted” by a professional intermediary 6. advocacy—whether the OGD is in a form and context that is supportive for use in advocacy (or other purposes) on behalf of marginalized and other groups and individuals 7. governance—whether the OGD process includes representation from the broad public in its overall policy development and governance (not just lawyers, techies and public servants).