Group items tagged

In January 2013, astronomers used Kepler's data to estimate that there could be at least 17 billion Earth-sized exoplanets in the Milky Way galaxy.

The number of confirmed planets frequently increases because as scientists analyse the data they are able publish their results online immediately. But as the finds are not yet peer reviewed, the total figure remains subject to change.

"That's why the other catalogues just lag behind. The review is reliable as it's exactly the same as what the journals do."

"no consensus for the definition of a planet" a

"Some objects, like some Kepler planets, are declared 'confirmed planets' but have not been published in [referenced] articles. It does not mean that they will not be published later on, but it introduces another fuzziness in the tally," he added.

"I don't just want to know where the exoplanets are, I want to understand the stars, because they are the hosts for the planets. I want to understand the whole galaxy and the distribution of the stars because everything is connected," he explained.

For him, the most exciting discoveries are Earth-like planets which could be habitable.

This planet likely has the same mass as Earth but is outside the "habitable zone" as it circles its star far closer than Mercury orbits our Sun.

In stage 1, they submit the introduction, methods, proposed analysis, and if necessary, pilot data. This manuscript then goes through the usual peer review process, and is assessed on criteria such as the soundness of the methods and analysis, and overall plausibility of the stated hypotheses.

Once researchers have passed through stage 1, they can then move on to data collection. In stage 2, they then submit the full manuscript – the introduction and agreed methods from stage 1, plus results and discussion sections. The results must include the outcome of the analyses agreed in stage 1, but the researchers are allowed to include additional analyses in a separate, ‘exploratory’ section (as long as they are justified).

Pre-registering scientific articles in this way helps to protect against a number of undesirable practices (such as p-hacking and HARKing) that can exaggerate statistical findings and make non-existent effects seem real. While this is a problem across psychology generally, it is a particularly extreme problem for violent video game research.

By outlining the intended methods and analysis protocols beforehand, Registered Reports protect against these problems, as the review process concentrates on the robustness of the proposed methods. And Registered Reports offer an additional advantage: because manuscripts are never accepted based on the outcome of the data analysis, the process is immune to researcher party lines. It doesn’t matter which research ‘camp’ you are in; your data – and just as importantly, your methods - will speak for themselves.

To understand why this matters, you have to know a little bit about the many tribes within the world of economics.

There are the academic economists who study the forces shaping the modern economy. Their work is rigorous but often obscure. Some of them end up in important policy jobs (See: Bernanke, B.) or write books for a mass audience (Piketty, T.), but many labor in the halls of academia for decades writing carefully vetted articles for academic journals that are rigorous as can be but are read by, to a first approximation, no one.

Then there are the economists in what can broadly be called the business forecasting community. They wear nicer suits than the academics, and are better at offering a glib, confident analysis of the latest jobs numbers delivered on CNBC or in front of a room full of executives who are their clients. They work for ratings firms like S.&P., forecasting firms like Macroeconomic Advisers and the economics research departments of all the big banks.

they are trying to do the practical work of explaining to clients — companies trying to forecast future demand, investors trying to allocate assets — how the economy is likely to evolve. They’re not really driven by ideology, or by models that are rigorous enough in their theoretical underpinnings to pass academic peer review. Rather, their success or failure hinges on whether they’re successful at giving those clients an accurate picture of where the economy is heading.

worries that income inequality is a factor behind subpar economic growth over the last five years (and really the last 15 years) is going from an idiosyncratic argument made mainly by left-of center economists to something that even the tribe of business forecasters needs to wrestle with.

Because the affluent tend to save more of what they earn rather than spend it, as more and more of the nation’s income goes to people at the top income brackets, there isn’t enough demand for goods and services to maintain strong growth, and attempts to bridge that gap with debt feed a boom-bust cycle of crises, the report argues. High inequality can feed on itself, as the wealthy use their resources to influence the political system toward policies that help maintain that advantage, like low tax rates on high incomes and low estate taxes, and underinvestment in education and infrastructur

The report itself does not break any major new analytical or empirical ground. It spends many pages summarizing the findings of various academic and government economists who have studied inequality and its discontents, and stops short of recommending any radical policy changes

Many of these journals have names that closely resemble those of established publications, making them easily mistakable. There is the Journal of Economics and Finance, published by Springer, but now also the Journal of Finance and Economics. There is the Journal of Engineering Technology, put out by the American Society for Engineering Education, but now another called the GSTF Journal of Engineering Technology.

Predatory journals have few expenses, since they do not seriously review papers that are submitted and they publish only online. They blast emails to academics, inviting them to publish. And the journals often advertise on their websites that they are indexed by Google Scholar. Often that is correct — but Google Scholar does not vet the journals it indexes.

The number of such journals has exploded to more than 10,000 in recent years, with nearly as many predatory as legitimate ones. “Predatory publishing is becoming an organized industry,” wrote one group of critics in a paper in Nature

Participating in such dubious enterprises carries few risks. Dr. Pyne, who did a study of his colleagues publications, reports that faculty members at his school who got promoted last year had at least four papers in questionable journals. All but one academic in 10 who won a School of Business and Economics award had published papers in these journals. One had 10 such articles.

Academics get rewarded with promotions when they stuff their resumes with articles like these, Dr. Pyne concluded. There are few or no adverse consequences — in fact, the rewards for publishing in predatory journals were greater than for publishing in legitimate ones.

Some say the academic system bears much of the blame for the rise of predatory journals, demanding publications even from teachers at places without real resources for research and where they may have little time apart from teaching.At Queensborough, faculty members typically teach nine courses per year. At four-year colleges, faculty may teach four to six courses a year.

Recently a group of researchers who invented a fake academic: Anna O. Szust. The name in Polish means fraudster. Dr. Szust applied to legitimate and predatory journals asking to be an editor. She supplied a résumé in which her publications and degrees were total fabrications, as were the names of the publishers of the books she said she had contributed to.The legitimate journals rejected her application immediately. But 48 out of 360 questionable journals made her an editor. Four made her editor in chief. One journal sent her an email saying, “It’s our pleasure to add your name as our editor in chief for the journal with no responsibilities.”

Open access got its start about a decade ago and quickly won widespread acclaim with the advent of well-regarded, peer-reviewed journals like those published by the Public Library of Science, known as PLoS. Such articles were listed in databases like PubMed, which is maintained by the National Library of Medicine, and selected for their quality.

Jeffrey Beall, a research librarian at the University of Colorado in Denver, has developed his own blacklist of what he calls “predatory open-access journals.” There were 20 publishers on his list in 2010, and now there are more than 300. He estimates that there are as many as 4,000 predatory journals today, at least 25 percent of the total number of open-access journals.

Having to take a test in swimwear seems not to distract most young men, but it does distract many young women, almost certainly due to the absurd beauty standards to which they are disproportionately subjected. In the words of the researchers, the swimsuit study effectively demonstrated “the psychological costs of raising girls in a culture that persistently objectifies the female body.”

Now, swimsuit season, especially for adolescent girls, no longer ends with summer because girls are using social media to share carefully crafted bikini shots all the time.

these are images with which girls become especially engaged. According to Dr. Walsh, “Girls are not comparing themselves to media ideals as much as one would expect, but they are making micro-comparisons to their peers. It’s not me versus Gisele Bündchen in a bikini, it’s me versus my good friend Amy in our bikinis.”

A new review of studies looking at the psychological effects of social media found that young people who spend a lot of time appraising their friends’ online photos ultimately feel worse about their own bodies.

Online imagery allows adolescents to observe one another in detail and gives unprecedented power to the age-old teenage preoccupation with appearance. We want to help our teenagers minimize the harm that can come from social media use, but the deluge of digital activity can make it hard to know where to start.

If replication is what separates the rigor of science from the squishiness of pseudoscience, where do we put all these rigorously validated findings that can no longer be proved? Which results should we believe?

Schooler demonstrated that subjects shown a face and asked to describe it were much less likely to recognize the face when shown it later than those who had simply looked at it. Schooler called the phenomenon “verbal overshadowing.”

The most likely explanation for the decline is an obvious one: regression to the mean. As the experiment is repeated, that is, an early statistical fluke gets cancelled out. The extrasensory powers of Schooler’s subjects didn’t decline—they were simply an illusion that vanished over time.

yet Schooler has noticed that many of the data sets that end up declining seem statistically solid—that is, they contain enough data that any regression to the mean shouldn’t be dramatic. “These are the results that pass all the tests,” he says. “The odds of them being random are typically quite remote, like one in a million. This means that the decline effect should almost never happen. But it happens all the time!

this is why Schooler believes that the decline effect deserves more attention: its ubiquity seems to violate the laws of statistics

In 2001, Michael Jennions, a biologist at the Australian National University, set out to analyze “temporal trends” across a wide range of subjects in ecology and evolutionary biology. He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance.

Jennions admits that his findings are troubling, but expresses a reluctance to talk about them

publicly. “This is a very sensitive issue for scientists,” he says. “You know, we’re supposed to be dealing with hard facts, the stuff that’s supposed to stand the test of time. But when you see these trends you become a little more skeptical of things.”

While publication bias almost certainly plays a role in the decline effect, it remains an incomplete explanation. For one thing, it fails to account for the initial prevalence of positive results among studies that never even get submitted to journals. It also fails to explain the experience of people like Schooler, who have been unable to replicate their initial data despite their best efforts.

Jennions, similarly, argues that the decline effect is largely a product of publication bias, or the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for

Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments.

One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”

suspects that an equally significant issue is the selective reporting of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.

after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results. Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting is everywhere in science, I got quite depressed,” Palmer told me. “As a researcher, you’re always aware that there might be some nonrandom patterns, but I had no idea how widespread it is.”

Palmer summarized the impact of selective reporting on his field: “We cannot escape the troubling conclusion that some—perhaps many—cherished generalities are at best exaggerated in their biological significance and at worst a collective illusion nurtured by strong a-priori beliefs often repeated.”

Palmer emphasizes that selective reporting is not the same as scientific fraud. Rather, the problem seems to be one of subtle omissions and unconscious misperceptions, as researchers struggle to make sense of their results. Stephen Jay Gould referred to this as the “sho

horning” process.

“A lot of scientific measurement is really hard,” Simmons told me. “If you’re talking about fluctuating asymmetry, then it’s a matter of minuscule differences between the right and left sides of an animal. It’s millimetres of a tail feather. And so maybe a researcher knows that he’s measuring a good male”—an animal that has successfully mated—“and he knows that it’s supposed to be symmetrical. Well, that act of measurement is going to be vulnerable to all sorts of perception biases. That’s not a cynical statement. That’s just the way human beings work.”

For Simmons, the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.

John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.”

In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals.

the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.

the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”

According to Ioannidis, the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher.

One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials.

The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong.

“It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies

That’s why Schooler argues that scientists need to become more rigorous about data collection before they publish. “We’re wasting too much time chasing after bad studies and underpowered experiments,”

The current “obsession” with replicability distracts from the real problem, which is faulty design.

“Every researcher should have to spell out, in advance, how many subjects they’re going to use, and what exactly they’re testing, and what constitutes a sufficient level of proof. We have the tools to be much more transparent about our experiments.”

Schooler recommends the establishment of an open-source database, in which researchers are required to outline their planned investigations and document all their results. “I think this would provide a huge increase in access to scientific work and give us a much better way to judge the quality of an experiment,”

scientific research will always be shadowed by a force that can’t be curbed, only contained: sheer randomness. Although little research has been done on the experimental dangers of chance and happenstance, the research that exists isn’t encouraging.

The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise. The hyperactivity of those coked-up Edmonton mice wasn’t an interesting new fact—it was a meaningless outlier, a by-product of invisible variables we don’t understand.

The problem, of course, is that such dramatic findings are also the most likely to get published in prestigious journals, since the data are both statistically significant and entirely unexpected

This suggests that the decline effect is actually a decline of illusion. While Karl Popper imagined falsification occurring with a single, definitive experiment—Galileo refuted Aristotelian mechanics in an afternoon—the process turns out to be much messier than that.

Many scientific theories continue to be considered true even after failing numerous experimental tests.

Even the law of gravity hasn’t always been perfect at predicting real-world phenomena. (In one test, physicists measuring gravity by means of deep boreholes in the Nevada desert found a two-and-a-half-per-cent discrepancy between the theoretical predictions and the actual data.)

Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling. Not because it reveals the human fallibility of science, in which data are tweaked and beliefs shape perceptions. (Such shortcomings aren’t surprising, at least for scientists.) And not because it reveals that many of our most exciting theories are fleeting fads and will soon be rejected. (That idea has been around since Thomas Kuhn.)

The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that’s often not the case. Just because an idea is true doesn’t mean it can be proved. And just because an idea can be proved doesn’t mean it’s true. When the experiments are done, we still have to choose what to believe. ♦

A classic example of this endemic bias at work is illustrated through Einstein. He was disturbed by the implications of an expanding universe. For thousands of years it was assumed — outside of some theological circles — that matter was eternal. The notion that it came into being at a discreet point in time naturally implied that something had caused it and quite possibly that that something had done it on purpose. Not willing to accept this new information, Einstein added a now famous “fudge factor” to his equations to maintain the solid state universe that he was comfortable with — something he would later describe as “the greatest blunder of my career.”

If there is great resistance to notions of design and causality in science, it is exponentially greater when it comes to theology.

beauty is very much about familiarity and it’s reinforcing an idea we have already. It’s like when we go on holiday, all we really want to do is take the photograph that we’ve seen in the brochure. Because our idea of beauty is constructed, by family, friends, education, nationality, race, religion, politics, all these things

I have found the 21st-century version of the Venetian secret and it is a mathematical formula. What you do, you get a half-decent, non-offensive kind of idea, then you times it by the number of studio assistants, and then you divide it with an ambitious art dealer, and that equals number of oligarchs and hedge fund managers in the world.

the nearest we have to an empirical measure of art that actually does exist is the market. By that reckoning, Cézanne’s “Card Players” is the most beautiful lovely painting in the world. I find it a little bit clunky-kitsch but that’s me. It’s worth $260m.

The opposite arguments are that it’s art for art’s sake and that’s a very idealistic position to take. Clement Greenberg, a famous art critic in the 1950s, said that art will always be tied to money by an umbilical cord of gold, either state money or market money. I’m pragmatic about it: one of my favourite quotes is you’ll never have a good art career unless your work fits into the elevator of a New York apartment block.

there’s one thing about that red painting that ends up in Sotheby’s. It’s not just any old red painting. It is a painting that has been validated. This is an important word in the art world and the big question is: who validates? There is quite a cast of characters in this validation chorus that will kind of decide what is good art. They are a kind of panel, if you like, that decides on what is good quality, what are we going to end up looking at?

They include artists, teachers, dealers, collectors, critics, curators, the media, even the public maybe. And they form this lovely consensus around what is good art.

there were four stages to the rise of an artist. Peers, serious critics and collectors, dealers, then the public.

Another member of that cast of validating characters is the collectors. In the 1990s, if Charles Saatchi just put his foot over the threshold of your exhibition, that was it. The media was agog and he would come in and Hoover it up. You do want the heavyweight collector to buy your work because that gives it kudos. You don’t want a tacky one who is just buying it to glitz up their hallway.

The next part of this chorus of validation are the dealers. A good dealer brand has a very powerful effect on the reputation of the artist; they form a part of placing the work. This is a slightly mysterious process that many people don’t quite understand but a dealer will choose where your work goes so it gains the brownie points, so the buzz around it goes up.

now, of course, galleries like the Tate Modern want a big name because visitor numbers, in a way, are another empirical measure of quality. So perhaps at the top of the tree of the validation cast are the curators, and in the past century they have probably become the most powerful giver-outers of brownie points in the art world.

ach of the encounters with these members of the cast of validation bestows upon the work, and on the artist, a patina, and what makes that patina is all these hundreds of little conversations and reviews and the good prices over time. These are the filters that pass a work of art through into the canon.

So what does this lovely consensus, that all these people are bestowing on this artwork, that anoints it with the quality that we all want, boil down to? I think in many ways what it boils down to is seriousness. That’s the most valued currency in the art world.

The whole idea of quality now seems to be contested, as if you’re buying into the language of the elite by saying, “Oh, that’s very good.” How you might judge this work is really problematic because to say it’s not beautiful is to put the wrong kind of criteria on it. You might say, “Oh, it’s dull!” [And people will say] “Oh, you’re just not understanding it with the right terms.” So I think, “Well, how do we judge these things?” Because a lot of them are quite politicised. There’s quite a right-on element to them, so do we judge them on how ethical they are, or how politically right-on they are?

What I am attempting to explain is how the art we see in museums and in galleries around the world, and in biennales – how it ends up there, how it gets chosen. In the end, if enough of the right people think it’s good, that’s all there is to it. But, as Alan Bennett said when he was a trustee of the National Gallery, they should put a big sign up outside saying: “You don’t have to like it all.”

Or then again I might say, “Well, what do I judge them against?” Do I judge them against government policy? Do I judge them against reality TV? Because that does participation very well. So, in the end, what do we do? What happens to this sort of art when it doesn’t have validation? What is it left with? It’s left with popularity.

Then, of course, the next group of people we might think about in deciding what is good art is the public. Since the mid-1990s, art has got a lot more media attention. But popularity has always been a quite dodgy quality [to have]. The highbrow critics will say, “Oh, he’s a bit of a celebrity,” and they turn their noses up about people who are well known to the public

Geraint Rees, director of University College London's Institute of Cognitive Neuroscience, complains that reading Tallis is "a bit like trying to nail jelly to the wall." He "rubbishes every current theory of the relationship between mind and brain, whether philosophical or neuroscientific," while offering "little or no alternative,"

cultural memes. The Darwinesque concept originates in Dawkins's 1976 book, The Selfish Gene. Memes are analogous to genes, Dennett has said, "replicating units of culture" that spread from mind to mind like a virus. Religion, chess, songs, clothing, tolerance for free speech—all have been described as memes. Tallis considers it absurd to talk of a noun-phrase like "tolerance for free speech" as a discrete entity. But Dennett argues that Tallis's objections are based on "a simplistic idea of what one might mean by a unit." Memes aren't units? Well, in that spirit, says Dennett, organisms aren't units of biology, nor are species—they're too complex, with too much variation. "He's got to allow theory to talk about entities which are not simple building blocks," Dennett says.

How is it that he perceives the glass of water on the table? How is it that he feels a sense of self over time? How is it that he can remember a patient he saw in 1973, and then cast his mind forward to his impending visit to the zoo? There are serious problems with trying to reduce such things to impulses in the brain, he argues. We can explain "how the light gets in," he says, but not "how the gaze looks out." And isn't it astonishing, he adds, that much neural activity seems to have no link to consciousness? Instead, it's associated with things like controlling automatic movements and regulating blood pressure. Sure, we need the brain for consciousness: "Chop my head off, and my IQ descends." But it's not the whole story. There is more to perceptions, memories, and beliefs than neural impulses can explain. The human sphere encompasses a "community of minds," Tallis has written, "woven out of a trillion cognitive handshakes of shared attention, within which our freedom operates and our narrated lives are led." Those views on perception and memory anchor his attack on "neurobollocks." Because if you can't get the basics right, he says, then it's premature to look to neuroscience for clues to complex things like love.

Yes, many unanswered questions persist. But these are early days, and neuroscience remains immature, says Churchland, a professor emerita of philosophy at University of California at San Diego and author of the subfield-spawning 1986 book Neurophilosophy. In the 19th century, she points out, people thought we'd never understand light. "Well, by gosh," she says, "by the time the 20th century rolls around, it turns out that light is electromagnetic radiation. ... So the fact that at a certain point in time something seems like a smooth-walled mystery that we can't get a grip on, doesn't tell us anything about whether some real smart graduate student is going to sort it out in the next 10 years or not."

Dennett claims he's got much of it sorted out already. He wrote a landmark book on the topic in 1991, Consciousness Explained. (The title "should have landed him in court, charged with breach of the Trade Descriptions Act," writes Tallis.) Dennett uses the vocabulary of computer science to explain how consciousness emerges from the huge volume of things happening in the brain all at once. We're not aware of everything, he tells me, only a "limited window." He describes that stream of consciousness as "the activities of a virtual machine which is running on the parallel hardware of the brain." "You—the fruits of all your experience, not just your genetic background, but everything you've learned and done and all your memories—what ties those all together? What makes a self?" Dennett asks. "The answer is, and has to be, the self is like a software program that organizes the activities of the brain."

‘Well, I’m not an engineer, but I think the bridge will stand up.’  ”

“Not just as a public figure, but as a human being, your fidelity should be to reality and to the truth,”

Among those agreeing that climate change is both real and a man-made threat are the Intergovernmental Panel on Climate Change, NASA, the National Academy of Sciences, the Defense Department, the American Association for the Advancement of Science and the American Meteorological Society

giving parents a “measure of choice” on vaccination is “the balance that government has to decide.”

murkiness of those comments caused alarm among public-health officials, who say the impact of the anti-vaccination movement is being seen in a measles outbreak in a number of states and Washington, D.C.

Climate change also sparks tension.

He said he was galled by U.S. Sen. Rand Paul’s recent assertion that the government should not require parents to vaccinate their children because it’s an issue of “freedom.”

“There is an unwritten litmus test for GOP officeholders” to express some form of skepticism about the phenomenon, he said.

However, Cruz, Rubio, Portman and Paul all voted against another amendment that said human activity contributes “significantly” to the threat. Cruz has asserted to the National Journal that climate change is “a theory that can’t be proven or disproven.”

In a separate vote, 98 senators — including Cruz, Rubio, Portman and Paul — acknowledged that climate change is “real and not a hoax.”

The group that denied climate change is occurring has pivoted, acknowledging that it exists. Still, the group questions whether it is a man-made phenomenon.

As for the caveat I’m not a scientist, “What they’re saying they implicitly think is that scientists don’t even know about climate change,”

Conservatives felt more negative emotions when they read scientific studies that challenged their views on climate change and evolution than liberals did in reading about nuclear power and fracking, but researchers believe that’s because climate change and evolution are more national in scope than the issues picked for liberals.

“The point is, to a very high level, scientists do know.”

Even those who agree that climate change is real and is man-made might not support government action

He said the disconnect between the public and scientists isn’t necessarily a bad thing

Such a slowdown “gives the science time to mature on some of these issues.”

most would-be candidates want to appeal to as many people as possible.

“And if you can sort of try to obscure your actual position but not offend anyone, that’s what I think they try to do,”

But it’s possible that their comments reflect a growing disconnect between the views of the public and the scientific community.

86 percent of scientists who are members of the American Association for the Advancement of Science said childhood vaccines such as the one for measles-mumps-rubella should be required, 68 percent of U.S. adults agreed.

larger gap on the subject of climate change: 87 percent of the scientists said climate change is caused mostly by human activity, while 50 percent of U.S. adults did.

The divide is not necessarily a conservative one

For example, while 88 percent of scientists said it is generally safe to eat genetically modified foods, only 37 percent of U.S. adults agreed.

And the vaccine issue is one that has united some liberals, the religious right and libertarians.

The study found that conservatives tend to distrust science on issues such as climate change and evolution. For liberals, it is fracking and nuclear power.

didn’t stop 39 Republicans — including GOP presidential contenders Sens. Ted Cruz, R-Texas, and Marco Rubio, R-Fla. — from opposing an amendment last month that blamed changing global temperatures on human activity.

liberals showed some distrust about science when they read about climate change and evolution

“Liberals can be just as biased as conservatives,” he said.

Rosenberg said the Internet can provide affirmation of pre-existing beliefs rather than encouraging people to find objective sources of information, such as peer-reviewed journals.

Often, attacking science is the easiest way to justify inaction, Rosenberg said.

We conducted two online surveys of a total of 1,258 adults in the United States. Each participant completed a variety of standard questionnaires to measure his or her impulsiveness, ethnocentrism and personal insecurity. (Professors Chua and Rubenfeld describe insecurity as “a goading anxiety about oneself and one’s place in society.” Since this concept was the most complex and counterintuitive element of their theory, we measured it several different ways, each of which captured a slightly different aspect.)Next, the participants completed a test of their cognitive abilities. Then they reported their income, occupation, education and other achievements, such as receiving artistic, athletic or leadership awards, all of which we combined to give each person a single score for overall success. Finally, our participants indicated their age, sex and parents’ levels of education.

First, the more successful participants had higher cognitive ability, more educated parents and better impulse control.

This finding is exactly what you would expect from accepted social science. Long before “The Triple Package,” researchers determined that the personality trait of conscientiousness, which encompasses the triple package’s impulse control component, was an important predictor of success — but that a person’s intelligence and socioeconomic background were equally or even more important.

Our second finding was that the more successful participants did not possess greater feelings of ethnocentrism or personal insecurity. In fact, for insecurity, the opposite was true: Emotional stability was related to greater success.

Finally, we found no special “synergy” among the triple package traits. According to Professors Chua and Rubenfeld, the three traits have to work together to create success — a sense of group superiority creates drive only in people who also view themselves as not good enough, for example, and drive is useless without impulse control. But in our data, people scoring in the top half on all three traits were no more successful than everyone else.

To be clear, we have no objection to Professors Chua and Rubenfeld’s devising a novel social-psychological theory of success. During the peer-review process before publication, our paper was criticized on the grounds that a theory created by law professors could not have contributed to empirical social science, and that ideas published in a popular book did not merit evaluation in an academic journal.We disagree. Outsiders can make creative and even revolutionary contributions to a discipline, as the psychologists Amos Tversky and Daniel Kahneman did for economics. And professors do not further the advancement of knowledge by remaining aloof from debates where they can apply their expertise. Researchers should engage the public, dispel popular myths and even affirm “common sense” when the evidence warrants.

it did not stand up to direct empirical tests. Our conclusion regarding “The Triple Package” is expressed by the saying, “What is new is not correct, and what is correct is not new.”

He came down hard on nutrition in a pull-no-punches 2013 British Medical Journal editorial titled, “Implausible results in human nutrition research,” in which he noted, “Almost every single nutrient imaginable has peer reviewed publications associating it with almost any outcome.”

Ioannidis told me that sussing out the connection between diet and health — nutritional epidemiology — is enormously challenging, and “the tools that we’re throwing at the problem are not commensurate with the complexity and difficulty of the problem.” The biggest of those tools is observational research, in which we collect data on what people eat, and track what happens to them.

He lists plant-based foods — fruit, veg, whole grains, legumes — but acknowledges that we don’t understand enough to prescribe specific combinations or numbers of servings.

funding bias isn’t the only kind. “Fanatical opinions abound in nutrition,” Ioannidis wrote in 2013, and those have bias power too.

“Definitive solutions won’t come from another million observational papers or small randomized trials,” reads the subtitle of Ioannidis’s paper. His is a burn-down-the-house ethos.

When it comes to actual dietary recommendations, the disagreement is stark. “Ioannidis and others say we have no clue, the science is so bad that we don’t know anything,” Hu told me. “I think that’s completely bogus. We know a lot about the basic elements of a healthy diet.”

Give tens of thousands of people that FFQ, and you end up with a ginormous repository of possible correlations. You can zero in on a vitamin, macronutrient or food, and go to town. But not only are you starting with flawed data, you’ve got a zillion possible confounding variables — dietary, demographic, socioeconomic. I’ve heard statisticians call it “noise mining,” and Ioannidis is equally skeptical. “With this type of data, you can get any result you want,” he said. “You can align it to your beliefs.”

Big differences in what people eat track with other differences. Heavy plant-eaters are different from, say, heavy meat-eaters in all kinds of ways (income, education, physical activity, BMI). Red meat consumption correlates with increased risk of dying in an accident as much as dying from heart disease. The amount of faith we put in observational studies is a judgment call.

I find myself in Ioannidis’s camp. What have we learned, unequivocally enough to build a consensus in the nutrition community, about how diet affects health? Well, trans-fats are bad.

Over and over, large population studies get sliced and diced, and it’s all but impossible to figure out what’s signal and what’s noise. Researchers try to do that with controlled trials to test the connections, but those have issues too. They’re expensive, so they’re usually small and short-term. People have trouble sticking to the diet being studied. And scientists are generally looking for what they call “surrogate endpoints,” like increased cholesterol rather than death from heart disease, since it’s impractical to keep a trial going until people die.

, what do we do? Hu and Ioannidis actually have similar suggestions. For starters, they both think we should be looking at dietary patterns rather than single foods or nutrients. They also both want to look across the data sets. Ioannidis emphasizes transparency. He wants to open data to the world and analyze all the data sets in the same way to see if “any signals survive.” Hu is more cautious (partly to safeguard confidentiality

I have a suggestion. Let’s give up on evidence-based eating. It’s given us nothing but trouble and strife. Our tools can’t find any but the most obvious links between food and health, and we’ve found those already.

Instead, let’s acknowledge the uncertainty and eat to hedge against what we don’t know

We’ve got two excellent hedges: variety and foods with nutrients intact (which describes such diets as the Mediterranean, touted by researchers). If you severely limit your foods (vegan, keto), you might miss out on something. Ditto if you eat foods with little nutritional value (sugar, refined grains). Oh, and pay attention to the two things we can say with certainty: Keep your weight down, and exercise.

I used to say I could tell you everything important about diet in 60 seconds. Over the years, my spiel got shorter and shorter as truisms fell by the wayside, and my confidence waned in a field where we know less, rather than more, over time. I’m down to five seconds now: Eat a wide variety of foods with their nutrients intact, keep your weight down and get some exercise.

Bohannon had done similar work before — in 2013 he submitted a fake research paper to more than 300 open-access journals as part of a sting operation for the journal Science.

Studies like his are called “underpowered,” meaning that they aren’t designed to distinguish between a real effect and pure luck. A study with thousands of participants being measured for just a few effects is “powerful.” But one like Bohannon’s, with just five people per group being measured according to any of 18 different variables? Any number of factors unrelated to the study could cause one of the variables to fluctuate, allowing researchers to irresponsibly — but not untruthfully — state that eating chocolate while dieting helps you lose more weight.

A responsible scientist shouldn’t conduct a trial like this, Bohannon said, and a responsible scientific journal shouldn’t publish it. But Bohannon is not a nutrition scientist (he does have a PhD in molecular biology) and the International Archives of Medicine, he says, is not the most responsible journal.

According to Bohannon, the journal didn’t peer review his study or even edit it (and the study could have used an edit — “chocolate” is misspelled more than once).

“It’s the reporters,” he told The Post. “The reporters and ultimately the editors. … People who are on the health science beat need to treat it like science, and that has to come from the editors. And if you’re reporting on a scientific study you need to actually look at the paper, you need to talk to a source who has real scientific expertise.”

Bohannon said he didn’t have any ethical qualms about tricking his fellow journalists this way. “I didn’t lie to reporters, except about my name. And whenever they asked me a scientific question about the study I gave them a completely honest answer,” he said. “The whole point is that this was as bad as a lot of science that is considered ‘real’ science. It gets reported without people asking the right questions.”

The agency also relies on industry data to develop rules on chemical safety that is often kept confidential for business reasons.

So why would he want to prohibit his own agency from using these studies? It’s not a mystery. Time and again the Trump administration has put the profits of regulated industries over the health of the American people. Fundamental research on the effects of air pollution on public health has long been a target of those who oppose the E.P.A.’s air quality regulations, like the rule that requires power plants to reduce their mercury emissions.

Liu’s tomes—they tend to be tomes—have been translated into more than twenty languages, and the trilogy has sold some eight million copies worldwide. He has won China’s highest honor for science-fiction writing, the Galaxy Award, nine times, and in 2015 he became the first Asian writer to win the Hugo Award, the most prestigious international science-fiction prize

“The Three-Body Problem” takes its title from an analytical problem in orbital mechanics which has to do with the unpredictable motion of three bodies under mutual gravitational pull. Reading an article about the problem, Liu thought, What if the three bodies were three suns? How would intelligent life on a planet in such a solar system develop? From there, a structure gradually took shape that almost resembles a planetary system, with characters orbiting the central conceit like moons. For better or worse, the characters exist to support the framework of the story rather than to live as individuals on the page.

Concepts that seemed abstract to others took on, for him, concrete forms; they were like things he could touch, inducing a “druglike euphoria.” Compared with ordinary literature, he came to feel, “the stories of science are far more magnificent, grand, involved, profound, thrilling, strange, terrifying, mysterious, and even emotional

Pragmatic choices like this one, or like the decision his grandparents made when their sons were conscripted, recur in his fiction—situations that present equally unconscionable choices on either side of a moral fulcrum

The great flourishing of science fiction in the West at the end of the nineteenth century occurred alongside unprecedented technological progress and the proliferation of the popular press—transformations that were fundamental to the development of the genre

Joel Martinsen, the translator of the second volume of Liu’s trilogy, sees the series as a continuation of this tradition. “It’s not hard to read parallels between the Trisolarans and imperialist designs on China, driven by hunger for resources and fear of being wiped out,” he told me. Even Liu, unwilling as he is to endorse comparisons between the plot and China’s current face-off with the U.S., did at one point let slip that “the relationship between politics and science fiction cannot be underestimated.”

Speculative fiction is the art of imagining alternative worlds, and the same political establishment that permits it to be used as propaganda for the existing regime is also likely to recognize its capacity to interrogate the legitimacy of the status quo.

Liu has been criticized for peopling his books with characters who seem like cardboard cutouts installed in magnificent dioramas. Liu readily admits to the charge. “I did not begin writing for love of literature,” he told me. “I did so for love of science.”

Liu believes that this trend signals a deeper shift in the Chinese mind-set—that technological advances have spurred a new excitement about the possibilities of cosmic exploration.

Liu’s imagination is dauntingly capacious, his narratives conceived on a scale that feels, at times, almost hallucinogenic. The time line of the trilogy spans 18,906,450 years, encompassing ancient Egypt, the Qin dynasty, the Byzantine Empire, the Cultural Revolution, the present, and a time eighteen million years in the future

The first book is set on Earth, though some of its scenes take place in virtual reality; by the end of the third book, the scope of the action is interstellar and annihilation unfolds across several dimensions. The London Review of Books has called the trilogy “one of the most ambitious works of science fiction ever written.”

Although physics furnishes the novels’ premises, it is politics that drives the plots. At every turn, the characters are forced to make brutal calculations in which moral absolutism is pitted against the greater good

In Liu’s fictional universe, idealism is fatal and kindness an exorbitant luxury. As one general says in the trilogy, “In a time of war, we can’t afford to be too scrupulous.” Indeed, it is usually when people do not play by the rules of Realpolitik that the most lives are lost.

“I know what you are thinking,” he told me with weary clarity. “What about individual liberty and freedom of governance?” He sighed, as if exhausted by a debate going on in his head. “But that’s not what Chinese people care about. For ordinary folks, it’s the cost of health care, real-estate prices, their children’s education. Not democracy.”

Liu closed his eyes for a long moment and then said quietly, “This is why I don’t like to talk about subjects like this. The truth is you don’t really—I mean, can’t truly—understand.”

Liu explained to me, the existing regime made the most sense for today’s China, because to change it would be to invite chaos. “If China were to transform into a democracy, it would be hell on earth,”

It was an opinion entirely consistent with his systems-level view of human societies, just as mine reflected a belief in democracy and individualism as principles to be upheld regardless of outcomes

“I cannot escape and leave behind reality, just like I cannot leave behind my shadow. Reality brands each of us with its indelible mark. Every era puts invisible shackles on those who have lived through it, and I can only dance in my chains.

Chinese people of his generation were lucky, he said. The changes they had seen were so huge that they now inhabited a world entirely different from that of their childhood. “China is a futuristic country,” he said. “I realized that the world around me became more and more like science fiction, and this process is speeding up.”

“We have statues of a few martyrs, but we never—We don’t memorialize those, the individuals.” He took off his glasses and blinked, peering into the wide expanse of green and concrete. “This is how we Chinese have always been,” he said. “When something happens, it passes, and time buries the stories.”

Tyson praises science as an “exercise in finding what is true” that’s based on peer-reviewed experimentation backed by other experiments and counter-experiments that gives birth to an “emergent truth.” He points out that science is “not something to toy with.” “You can’t say, ‘I chose not to believe in E=mc2,’” he says, referring to physicist Albert Einstein’s corroborated theory of special relativity. “You don’t have that option. It is true, whether or not you believe in it.”

Tyson warns that every minute someone is in denial of a scientific truth delays the “political solution that should have been established years ago.”  “Recognize what science is, and allow to be what it can and should be: In the service of civilization,” he says. “It’s in our hands.”

Patients in this study had a mean age of 44 years, so were very much part of the young, working-age population. Only 18% had been hospitalised with COVID-19, meaning organ damage may occur even after a non-severe infection.

Another piece of early research (awaiting peer review) suggests that SARS-CoV-2 could also have a long-term impact on people’s organs.

Perhaps unsurprisingly, people with more severe disease initially – characterised by more than five symptoms – seem to be at increased risk of long COVID. Older age and being female also appear to be risk factors for having prolonged symptoms, as is having a higher body mass index.

Rather harder to explore is the symptom of fatigue. Another recent large-scale study has shown that this symptom is common after COVID-19 – occurring in more than half of cases – and appears unrelated to the severity of the early illness.

While men are at increased risk of severe infection, that women seem to be more affected by long COVID may reflect their different or changing hormone status.

Some symptoms of long COVID overlap with menopausal symptoms, and hormone replacement using medication may be one route to reducing the impact of symptoms.

What is clear, however, is that long-term symptoms after COVID-19 are common, and that research into the causes and treatments of long COVID will likely be needed long after the outbreak itself has subsided.