Dang, I teach all this stuff about genes and chromosomes and epigenetics, but I don’t have the advantage of giant floating holographic molecules floating around me. Maybe I’ll have to steal this for my classes.
Although it could use some discussion of Blaschko’s lines, to explain why you get a stripey pattern rather than just salt-and-pepper.
There’s a new vampire series on FX by Guillermo Del Toro, The Strain. I haven’t seen it — I don’t get that channel — but I’ve read the book, which I found interesting for making vampires utterly disgusting, and also for stealing biological analogues for the infection (alas, I thought the story started very well but got tedious by the end). Apparently, the model for the vampire parasite was the horsehair worm, or nematomorpha. These are best known as parasites of orthopterans.
I do have to object to one statement in that story: “Really, for my money, worms are among the worst animal groups out there.” Worms are not a proper taxon. The Nematomorpha are a completely different phylum from the worms most people are familiar with, from nematodes, from polychaetes, from flatworms, etc. Worms are phyletically diverse! Not all of them turn you into a vampire.
Michael Shermer indulges in some shabby Libertarian statistical games to wave away American economic inequality. Sure, there are inequities, he argues, but they’re not so bad — the poor are also getting slightly richer.
The rich are getting richer, as Brookings Institution economist Gary Burtless found by analyzing tax data from the Congressional Budget Office for after-tax income trends from 1979 through 2010 (including government assistance). The top-fifth income earners in the U.S. increased their share of the national income from 43 percent in 1979 to 48 percent in 2010, and the top 1 percent increased their share of the pie from 8 percent in 1979 to 13 percent in 2010. But note what has not happened: the rest have not gotten poorer. They’ve gotten richer: the income of the other quintiles increased by 49, 37, 36 and 45 percent, respectively.
I have a few problems with this. First, in an article titled “The Myth of Income Inequality”, he’s doing a bit of bait-and-switch: it doesn’t matter if the baseline is rising, the question is about the disparity. Income disparity is greater now than it was before. His own numbers show that.
This argument is basically a version of the “Well, the poor all have cell phones!” dismissal. They’ve also got refrigerators and TVs, therefore, you should just ignore the fact that the wealthiest are sucking up all our prosperity to fund luxuries and frivolities. We should just pretend that we’re all getting the benefit of a rising tide, and never mind that yacht towering above your dinghy.
But there are other funny things going on in this economy. Look at this chart: the costs of TVs and toys and cell phones (the latter at least is essential now; but it’s a new cost for the poor, even if it is dropping) are plummeting, but the stuff that really matters for upward income mobility, like child care, health care, and education are going up. Especially education. We are saddling new graduates with overwhelming amounts of debt.
Another interesting game Shermer plays is to ignore the difference between income and wealth. It’s good that the poor are getting some increase in income, but if you’re using it to make ends meet or dig out from under a pile of debt, you’re not going to be accumulating any wealth — you can still get poorer. Meanwhile, the rich don’t have to worry about covering essential living expenses, and can invest and get richer. It’s useful to be able to see the distinction, so here’s a handy table of wealth and income in the US.
Income, net worth, and financial worth in the U.S. by percentile,
in 2010 dollars
Wealth or income class Mean household income Mean household net worth Mean household financial (non-home) wealth Top 1 percent $1,318,200 $16,439,400 $15,171,600 Top 20 percent $226,200 $2,061,600 $1,719,800 60th-80th percentile $72,000 $216,900 $100,700 40th-60th percentile $41,700 $61,000 $12,200 Bottom 40 percent $17,300 -$10,600 -$14,800From Wolff (2012); only mean figures are available, not medians. Note that income and wealth are separate measures; so, for example, the top 1% of income-earners is not exactly the same group of people as the top 1% of wealth-holders, although there is considerable overlap.
Here’s another sneaky trick. When the concern is inequality, let’s ignore the most extreme and instead focus on perceptions.
One reason for the controversy is that people overestimate differences between the rich and poor. In a 2013 study published in Psychological Science entitled “Better Off Than We Know,” St. Louis University psychologist John R. Chambers and his colleagues found that most people estimate that the richest 20 percent make 31 times more than the poorest 20 percent (it is 15.5 times), and they believe that the average annual income of the richest 20 percent of Americans is $2 million, whereas in fact it is $169,000, a perceptual difference of nearly 12 times. “Almost all of our study participants,” the authors concluded, “grossly underestimated Americans’ average household incomes and overestimated the level of income inequality.”
That’s beautiful sleight of hand. First, as previously mentioned, talk only about income, not wealth (and most of us already have poor intuition about the difference; notice also that if you look at the table above, the guesses pretty much hit the mark on wealth, rather than income). Then talk only about the top 20%, rather than the top 1%. And then make much of the fact that people’s guesses about rich people’s incomes are wrong. Har har, the proles guessed that managers make 31 times as much money as they do, when it’s really only 12 times.
Really? Try this exercise: imagine that you got paid just 10 times as much as you do now. “Just” 10 times. How much of a difference would that make in your life? I’m in a comfortable position; optimistically, I’m probably somewhere in the bottom of the 20%, so I don’t have to worry much about making ends meet, and give me an order of magnitude more money and I’d just be socking it away in a bank. But if you’re poor, if you’re struggling to cover child care and rent and keep the family fed, that’s an immense difference.
And of course the other factor is that the 20% aren’t actually working any harder than the 80% — their labor may require more training (which we’re trying hard to lock poor people out of with skyrocketing education costs), but they’re not actually working any harder than you are. My father was often working two jobs in order to keep spinning his wheels in poverty, so I’ve seen this inequality at work, and am well aware that I’m on the lucky side of the rich-poor divide.
But set aside all the squinty-eyed statistical games, and simply ask the fundamental question: Who owns the country? Where is the product of 315 million people’s labor going?
In the United States, wealth is highly concentrated in a relatively few hands. As of 2010, the top 1% of households (the upper class) owned 35.4% of all privately held wealth, and the next 19% (the managerial, professional, and small business stratum) had 53.5%, which means that just 20% of the people owned a remarkable 89%, leaving only 11% of the wealth for the bottom 80% (wage and salary workers). In terms of financial wealth (total net worth minus the value of one’s home), the top 1% of households had an even greater share: 42.1%.
That’s the inequality that we’re concerned about, that a mere 1% own well over a third of the wealth of the country, and it’s increasing — they use that wealth to manipulate media and politics to steal even greater quantities of our work. We are becoming a kleptocracy.
But never mind that. Look! Over there! There’s a poor person with an Xbox!
But wait! Even that claim that the poor have gotten richer may be dodgy: this analysis of reported incomes shows that we’ve been experiencing a decline.
In small towns across America, manly men are customizing their jacked-up diesel trucks to intentionally emit giant plumes of toxic smoke every time they rev their engines. They call it “rollin’ coal,” and it’s something they do for fun.
An entire subculture has emerged on the Internet surrounding this soot-spewing pastime—where self-declared rednecks gather on Facebook pages (16,000 collective followers) Tumblers and Instagram (156,714 posts) to share photos and videos of their Dodge Rams and GM Silverados purposefully poisoning the sky. As one of their memes reads: “Roll, roll, rollin’ coal, let the hybrid see. A big black cloud. Exhaust that’s loud. Watch the city boy flee.”
These dumbasses think they’re being tough and rebellious, when they’re simply parroting conservative stupidity.
Aside from being macho, the rollin’ coal culture is also a renegade one. Kids make a point of blowing smoke back at pedestrians, in addition to cop cars and rice burners (Japanese-made sedans), which can make it dangerously difficult to see out of the windshield. Diesel soot can also be a great road rage weapon should some wimpy looking Honda Civic ever piss you off. “If someone makes you mad, you can just roll coal, and it makes you feel better sometimes,” says Ryan, a high school senior who works at the diesel garage with Robbie. “The other day I did it to this kid who was driving a Mustang with his windows down, and it was awesome.”
The ultimate highway enemy, however, are “nature nuffies,” or people who drive hybrid cars, because apparently, pro-earth sentiment is an offense to the diesel-trucking lifestyle. “The feeling around here is that everyone who drives a small car is a liberal,” says Ryan. “I rolled coal on a Prius once just because they were tailing me.”
I despair of humanity sometimes. If we make it that far, future generations will look back aghast at these idiots. I know there is something at least that keeps me going.
According to the Clean Air Taskforce, diesel exhaust is one of the country’s greatest sources of toxic pollutants and leads to 21,000 premature deaths each year, but even that won’t deter the coal rollers. “I’m not a scientist, but it couldn’t be too horrible,” Robbie says. “There are a lot of factories that are doing way worse than my truck.”
I’m not ready to die off yet, because I want to see these motherfuckers die first.
Penn Jillete defends Anthony Cumia. We’re all missing the important detail that exonerates him — sure, he went off for hours in a racist tirade, but don’t you all realize that Cumia is licensed to carry a concealed handgun?
Penn Jillette says that people areburying the ledein the alleged incident that led to SiriusXM’s decision to fire “Opie and Anthony” host Anthony Cumia. He also argued that he’s never seen any evidence that suggests Cumia is racist as many of his critics have alleged.
We are burying the lede of this story, which is that Anthony, who has a reputation for being a bit of a hot head, is carrying, comes up gets hit in the face and does not hurt the person back,Jillette said during his “Penn’s Sunday School” podcast this weekend.
That is incredible. If I am in the position where I cross somebody who is carrying a gun and who can defend themselves and hurt me, and their choice is to write angry stuff on Twitter instead of fighting me back — wonderful. Gandhi! That’s Gandhi! … That is Martin Luther King,he continued.
Seriously? He just compared a racist ranter to Gandhi and Martin Luther King because he didn’t shoot a black woman in the face?
Sure. This sounds exactly like something King or Gandhi would say.
Savage violent animal fucks prey on white people. Easy targets. This CUNT has no clue how lucky she was. She belted me 10 times. I had a gun
No,an ANIMAL BITCH used it’s instinctual violence on me. I restrained myself from putting it to sleep
It’s a jungle out in our cities after midnight. Violent savages own the streets. They all came 2 defend this pig. I had to yell like at dogs
Gosh. I’ve never shot anyone, and I’ve never even raged at minorities as animals. I guess that means I must be like Jesus!
By the way, the article strangely features a photo of Cumia’s concealed carry license…with a 2012 expiration date. I guess True Skeptics™ don’t give a damn about little details like that.
We’re always talking about this curious phenomenon, that we see lots of women at the undergraduate and graduate level in biology, but large numbers of them leave science rather than rising through the ranks. Why is that? It seems that one answer is that elite male faculty in the life sciences employ fewer women, that is, the more prestigious, well-known labs headed by male faculty with great academic reputations tend not to hire women for the next level of training.
Women make up over one-half of all doctoral recipients in biology- related fields but are vastly underrepresented at the faculty level in the life sciences. To explore the current causes of women’s underrepresentation in biology, we collected publicly accessible data from university directories and faculty websites about the composition of biology laboratories at leading academic institutions in the United States. We found that male faculty members tended to employ fewer female graduate students and postdoctoral researchers (post-docs) than female faculty members did. Furthermore, elite male faculty—those whose research was funded by the Howard Hughes Medical Institute, who had been elected to the National Academy of Sciences, or who had won a major career award—trained significantly fewer women than other male faculty members. In contrast, elite female faculty did not exhibit a gender bias in employment patterns. New assistant professors at the institutions that we surveyed were largely comprised of postdoctoral researchers from these prominent laboratories, and correspondingly, the laboratories that produced assistant professors had an overabundance of male postdocs. Thus, one cause of the leaky pipeline in biomedical research may be the exclusion of women, or their self-selected absence, from certain high-achieving laboratories.
These statistics were obtained by sampling a large number of labs across the US. The leaky pipeline is rather obvious in this table: note that we have parity at the graduate student level, but that it falls off dramatically at the next level up.
This is a problem. One (not the only one!) of the criteria used to select academic hires is the reputation of the lab they came from — some labs are just really good at cranking out the data, publishing publishing publishing, and new graduates coming out of those labs are likely to continue that pattern. Coming out of a well-known lab provides a real leg-up for an academic career. But what this paper found is that women were less likely to find themselves in those labs.
We found that female trainees were much less likely to work for an elite PI, particularly at the post-doctoral level. Combining faculty of both genders, men were about 17% more likely to do their graduate training with a member of the NAS, 25% more likely to do their postdoctoral training with a member of the NAS, and 90% more likely to do their postdoctoral training with a Nobel Laureate. Thus, the gender skew in employment results in fewer women being trained in the laboratories of elite investigators.
Get with the program, Nobelists!
My first thought was that maybe this was a product of an older generation — that more senior faculty are going to be much older and perhaps unfortunately traditionalist, so all we have to do is wait for them to die off and be replaced. No such luck. When the data are carefully dissected, the correlation isn’t with age, but with elite status (as defined by membership in prestigious organizations). Young male investigators are just as unlikely as old male investigators to hire women.
As expected, among male faculty, elite status was negatively correlated with the percentage of female postdocs in a laboratory (P < 0.0001). This relationship remained true even when several other explanatory variables were added, including faculty rank, years since a faculty member had received his or her PhD, and total number of trainees in a laboratory. As a single independent variable, years since PhD was moderately negatively correlated with the percentage of female postdocs in laboratories with male faculty members (P < 0.045), but this effect disappeared when other variables were included in the model. This observation suggests that a faculty member’s age is not a significant determinant of the gender makeup of their laboratory, and both young and old elite professors employ few women. Laboratory size was also negatively correlated with the representation of female postdocs both as a single variable and in multivariable models. Regression against the percentage of female graduate students in each laboratory revealed similar, although less robust, results. In multivariable models, elite status was associated with a significantly lower percentage of female graduate students trained by male faculty. However, years since PhD correlated with an increasing representation of female graduate students, whereas laboratory size was not significantly correlated in either direction. Finally, we constructed equivalent linear models for female PIs, but we failed to find a single variable that was significantly associated with differential representation of female trainees in these laboratories.
The paper is careful to point out that they don’t know the direct causes of the differences, whether it’s exclusion, conscious or otherwise, by faculty men, or reluctance of women to apply to those labs. We should probably try to figure that one out, since that’s how the problem gets fixed…but it’s probably a combination of all of these factors.
Irrespective of the cause of the gender disparities in elite laboratories, its consequences significantly shape the academic ecosystem. Our data show that these laboratories function as gateways to the professoriate: new generations of faculty members are predominantly drawn from postdocs trained by high-achieving PIs. However, these feeder laboratories employ a disproportionate number of men. According to the theory of cumulative disadvantage, persistent inequalities in achievement can result from small differences in treatment over a prolonged goal-oriented process. In controlled studies, women in academia receive less favorable evaluations, receive lower salary offers, and are ignored by faculty more frequently than men. Access to training in certain laboratories may be another level at which women are disadvantaged. The absence or exclusion of female trainees from elite laboratories deprives them of the resources, visibility, networking opportunities, etc. that could facilitate their professional development. These differences may contribute to the leaky pipeline by shunting women toward laboratories that provide fewer opportunities for advancement in academic science.
I’m certainly not at one of those elite laboratories, so I can’t do much at that level — but I am training swarms of undergraduate women and stuffing them in at the base of the pipeline. One thing we can do here is encourage our graduates to be ambitious and push hard to get into the labs they really want…and to prepare them for the institutional biases that will get in the way.
The EU is sinking €1.2bn (and the US is proposing to spend more, $3 billion) into a colossal project to build a supercomputer simulation of the human brain. To which I say, “What the hell? We aren’t even close to building such a thing for a fruit fly brain, and you want to do that for an even more massive and poorly mapped structure? Madness!” It turns out that I’m not the only one thinking this way: European scientists are exasperated with the project.
"The main apparent goal of building the capacity to construct a larger-scale simulation of the human brain is radically premature," Peter Dayan, director of the computational neuroscience unit at UCL, told the Guardian.
"We are left with a project that can’t but fail from a scientific perspective. It is a waste of money, it will suck out funds from valuable neuroscience research, and would leave the public, who fund this work, justifiably upset," he said.
There is a place for Big Science. I’d suggest that when you’re at the preliminary exploratory stage, as we are with human brain function, it’s better to fund many small exploratory parties to map out the terrain, rather than launching a huge invasion with charts that are made out of speculation. We know a computer simulation is going to fail, because we don’t know what it’s going to simulate. So why are they doing this? Maybe it’s a question of who “they” are.
Alexandre Pouget, a signatory of the letter at Geneva University, said that while simulations were valuable, they would not be enough to explain how the brain works. "There is a danger that Europe thinks it is investing in a big neuroscience project here, but it’s not. It’s an IT project," he said. "They need to widen the scope and take advantage of the expertise we have in neuroscience. It’s not too late. We can fix it. It’s up to Europe to make the right decision."
I’ve noticed this, that a lot of gung-ho futurists and computer scientist types have this very naive vision of how the brain works — it’s just another computer. We can build those. Build a big enough computer, and it’ll be just like the brain. Nope. That’s operating on ignorance. And handing ignorant people billions of dollars to implement a glorious model of their ignorance is an exercise in futility.