Gravlee et al: Race, Genetics, Social Inequality, and Health

Clarence Gravlee, Amy Non and Connie Mulligan have just published an outstanding article in PLoS ONE, Genetic Ancestry, Social Classification, and Racial Inequalities in Blood Pressure in Southeastern Puerto Rico. The abstract opens:

The role of race in human genetics and biomedical research is among the most contested issues in science. Much debate centers on the relative importance of genetic versus sociocultural factors in explaining racial inequalities in health. However, few studies integrate genetic and sociocultural data to test competing explanations directly.

Note how that fits so well into the points just made in Nature/Nurture: Slash to the Rescue. But Gravlee, Non and Mulligan don’t just say we need to overcome the nature vs. nurture dichotomy, they do research that bridges it and even better, test ideas on both sides: “We draw on ethnographic, epidemiologic, and genetic data collected in southeastern Puerto Rico to isolate two distinct variables for which race is often used as a proxy: genetic ancestry versus social classification.”

This type of collaborative research can be crucial to getting the data to answer complicated questions. Connie Mulligan and Lance Gravlee deserve credit for taking the time to discuss how to bring together their respective approaches before going out to do research. In this case, the data come down more on the nurture (or social) side. As they write:

Our preliminary results provide the most direct evidence to date that previously reported associations between genetic ancestry and health may be attributable to sociocultural factors related to race and racism, rather than to functional genetic differences between racially defined groups.

Before someone gets all hot and bothered, Lance has also shown how to bring nurture back to nature. In Gravlee’s recent paper, How Race Becomes Biology: Embodiment of Social Inequality (pdf), he gives us following: “Drawing on recent developments in neighboring disciplines, I present a model for explaining how racial inequality becomes embodied – literally – in the biological well-being of racialized groups and individuals. This model requires a shift in the way we articulate the critique of race as bad biology.”

In the PLoS paper, Lance, Amy and Connie are aiming squarely at the use of race in medicine, where it has become common in some circles to use racial classification as a proxy for genetics. Basically this research destroys the proxy notion, since social classification turns out to be a better predictor of blood pressure than genetic ancestry.

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SurveyFail redax: Downey adds to Lende

Daniel did a posting earlier today on Sex, Lies and IRB Tape: Netporn to SurveyFail that explores a research project that self-immolated through bad design, horrible conflict management, and a number of other character flaws. I’m really glad Daniel did this because he’s the more tech-savvy half of this duo. I just saw this yesterday and started to read up on the commentary but quickly realized that I was over my head, having pretty much exhausted my ability to navigate communication technology and resulting subcultural movements with a Twitter-related post a while back.

But I did want to add a couple of points because I’m particularly interested in research design and ethics and because I like kicking researchers when they’re down. No, no, just kidding — because I find the focus of ‘evolutionary’ theorists on the supposed ‘hard wiring’ of sexuality to be one of the more irritating and, well, hard-wired theoretical assumptions, even in the face of OVERWHELMING evidence to the malleability of human sexuality.

I apologize for not putting up some clever graphic, but I spent most of today helping friends build their mud-brick house and then went to a Showground Association meeting, where I was elected president (that’s kind of like the County Fairground in my town). My brain’s fried, but I don’t want to let this post sit for too long or it’s moment will have well and truly passed.

Research ethics

In my brief and incomplete survey of the discussions of this research, it became obvious that slash fans were particularly irritated, not just by the initial bad research design, but also by the seeming inability to apologize, learn from criticism or even simply back off on the part of the researchers.

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Sex, Lies and IRB Tape: Netporn to SurveyFail

Slash Fail
Neuroscience researchers Ogi Ogas and Sai Gaddam have done a massive FAIL through bad research, failed ethics, and greed. They created an online survey targeted at slash fiction fans that was a debacle start to finish.

Slash fiction takes prominent characters from movies, television, and fiction and explores their relationships in unconventional ways. The founding example is Kirk/Spock, where the slash indicates a story about Kirk and Spock getting it on. The creators and consumers of slash fiction are generally women.

Earlier this year Ogi Ogas and Sai Gaddam signed a deal with Penguin for a popular book with the initial title “Rule 34: What Netporn Teaches Us about the Brain.” Rule 34 is simply that online “If it exists, there is porn on it. No exceptions.”

Slash fiction fans became one of their “netporn” targets. Ogas and Gaddam created and distributed their online survey that aimed to prove their basic premise (well, my take on it): “When in doubt, the brain causes everything. When that’s something we don’t really understand, then it must be the primitive parts of the brain.”

Here’s how I derived that premise. First comes shaggirl’s description of Ogas’ response to criticism (Note: Ogas took down the survey and the livejournal that discussed the project, so I am relying on people who have captured their words):

He defends his comparison of women liking slash to straight men liking transsexuals because “some deep sense of pleasure or satisfaction ultimately rooted in subcortical circuits” compels us to seek out slash/transsexuals despite fearing exposure to society at large.

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In praise of partial explanation (and flowcharts)

Created by RPM at Evolgen

Created by RPM at Evolgen

During our panel at the American Anthropology Association last year, Prof. Naomi Quinn warned that ‘a flowchart is not a theory.’ She stressed the limits to the explanatory power of a simple diagram; her skepticism, of course, is entirely warranted.

But since I was one of the prime offenders with the explanatory flowchart, and I seem to be using them more and more, I wanted to offer a stalwart defense of the use of flowcharts and diagramming in neuroanthropology, especially as both contribute to the practice of partial explanation. So, to pick up a theme from a number of my posts, ‘yes-you’re-right-but-I-still-disagree,’ here’s why I find flowcharts particularly useful and think anthropologists should be doing a lot more diagramming to highlight complex patterns of causation, situating more broadly the parts of complex systems that they are exploring.

But before I go any further, I need to direct all our readers to the recent announcement of the first Neuroanthropology conference which Daniel posted. Although I want to post, I feel like I also want to keep drawing attention to this announcement. But on with it…

As with all of her comments, I felt that Prof. Quinn cut to the quick, highlighting an issue in a cautionary fashion rather than rejecting specific arguments our panelists were making (at least I don’t think she was just calling me out…). In the case of flowcharts, Prof. Quinn suggested that diagramming relationships was a preliminary step, not a final goal – at least that’s one of the ways that I took her comments – and I agree.

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Measuring Process Not Belief: Shane Battier and Stress

The Wrong Box Score

The Wrong Box Score

Besides being a great read about Shane Battier and success in professional basketball, The No-Stats All-Star article by Michael Lewis in last Sunday’s New York Times Magazine carries a larger lesson about how our understanding of the world is shifting. One of its main points is that we are becoming increasingly statistics driven, with sports at the leading edge of this transformation. We can spend lots of money on stars, like the New York Yankees, or we look more closely at what actually leads to success and how we can achieve that with less money. Shane Battier epitomizes this change because his individual stats are rather mediocre, his physical skills rather normal for an NBA player. But he makes his team win.

The key question becomes, how does he do this? That is where Michael Lewis mixes qualitative research (interviews) and ethnographic insight (coming from Battier’s own experience) with an examination of new ways of measuring everything that might count about a basketball game. It’s a powerful mix.

For me it illustrates two important points about how we can develop better measures, ones that are closer to what actually determine outcomes and that don’t fall into so easily into measuring our own beliefs about the world. And yes, by “our own” I mean the researchers who come up with the measures. Here’s a relevant section describing Daryl Morey, the man behind the Houston Rockets new approach to figuring out what works:

What [Morey] will say, however, is that the big challenge on any basketball court is to measure the right things. The five players on any basketball team are far more than the sum of their parts; the Rockets devote a lot of energy to untangling subtle interactions among the team’s elements. To get at this they need something that basketball hasn’t historically supplied: meaningful statistics. For most of its history basketball has measured not so much what is important as what is easy to measure — points, rebounds, assists, steals, blocked shots — and these measurements have warped perceptions of the game. (“Someone created the box score,” Morey says, “and he should be shot.”) How many points a player scores, for example, is no true indication of how much he has helped his team.

Here is how it makes a difference. Battier doesn’t get great traditional stats – points scored, shots blocked, and so forth. But he does things that, on aggregate, make a bigger difference.

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US Presidential campaign wordpiles

What's on your mind?

What's on your mind?

The Boston Globe did a ‘Wordpile’ analysis of both Sen. John McCain and Sen. Barack Obama’s websites and generated some fascinating graphics. Check out the original here. There’s lots one could say about these graphics — the Globe only highlights a few of the fascinating terms, and I’d want to try to chase down the context of a few that show up prominently because they look pretty ambiguous — but some factors stand out clear as day. The most obvious is that ‘Obama’ is the most mentioned word on both blogs. ‘Veeeery inturusting…’

The reason I bring this visual up though is that I found it a fascinating, graphically powerful way to present a basic qualitative-quantitative bit of research. Although I’m intrigued by research tools like nVivo and Atlas.ti, I sometimes wish that there were richer ways to present the data. This ‘Wordpile’ output is rich enough to put on a t-shirt! I’ll have to find some way to integrate it into my seminars on hybrid research methods.

And if anyone knows where I can lay my hands on the software or script to generate this sort of thing, please send along the link. A quick search didn’t give me anything, and I don’t want to sit in my office all Friday obsessing about this.

Mind, body and Wiimote

Blogging on Peer-Reviewed ResearchI’m not usually the one blogging on video games; this tends to be Daniel’s department. After all, he’s got three boys at home, and I live with two horse-obsessed women, so it’s a bit out of my habitual orbit. I get more interaction with tractors than video game consoles. But Daniel tossed this reference my direction, and I decided to write on it for a number of reasons (thanks to Daniel).

According to a recent post on, ‘The Wiimote as an interface bridging mind and body,’ a research team led by Rick Dale at the University of Memphis has been using the Wiimote from Nintendo to study how people reach as they learn new tasks. As the Psyorg story discusses, the Memphis team taught people a symbol matching task and used the Wiimote to judge the quality of their movements when doing the task.

As people learned, their bodies reflected the confidence of that learning. Participants moved the Wiimote more quickly, more steadily, and also pressed on it more firmly as they became familiar with the symbols. While everyone knows that you get better at moving in tasks that require intricate movement (such as learning to use chopsticks), these results suggest that your body movements are related to learning other information as well.

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