Hard science vs. soft science and the science mystique

So, recently I’ve been doing a lot of thinking and reading about what it means to do science, what science entails  and what is (and is not) science. Partly, this was sparked by the  fact that, at a recent middle school science education event, I was asked more than once why linguistics counted as a science. This intrigued me, as no one at the Lego robots display next to us had their discipline’s qualifications questioned, despite the fact that engineering is not scientific. Rigorous, yes. Scientific, no.

Science01science

Hmm, I dunno. Looks science-y, but I don’t see any lab coats. Or goggles. There should definitely be more goggles.

This subject is particularly near and dear to me because my own research looks into, among other things, how the ways in which linguists gather data affect the data they gather and the potential for systematic bias that introduces. In order to look at how we do things, I also need to know why. And that’s where this discussion of science comes in. This can be a hard discussion to have, however, since conversations about what science is, or should be, tends to get muddied by the popular conception of science. I’m not saying people don’t know what science is, ’cause I think most people do, just that we (and I include myself in that) also have a whole bucketful of other socially-motivated ideas that we tend to lump in with science.

I’m going to call the social stuff that we’ve learned to associate with science The Science Mystique. I’m not the first person to call it that, but I think it’s fitting. (Note that if you’re looking for the science of Mystique, you’ll need to look elsewhere.) To start in our exploration of the Science Mystique, let’s start with a quote from another popular science writer, Phil Plait.

They [the scientists who made the discoveries discussed earlier in the speech] used physics. They used math. They used chemistry, biology, astronomy, engineering.

They used science.

These are all the things you discovered doing your projects. All the things that brought you here today.

Computers? Cell phones? Rockets to Saturn, probes to the ocean floor, PSP, gamecubes, gameboys, X-boxes? All by scientists.

Those places I talked about before? You can get to know them too. You can experience the wonder of seeing them for the first time, the thrill of discovery, the incredible, visceral feeling of doing something no one has ever done before, seen things no one has seen before, know something no one else has ever known.

No crystal balls, no tarot cards, no horoscopes. Just you, your brain, and your ability to think.

Welcome to science. You’re gonna like it here.

Inspirational! Science-y! Misleading! Wait, what?

So there are a couple things here that I find really troubling, and I’m just going to break them down and go though them one by one. These are things that are part of the science mystique, that permeate our cultural conception of what science is, and I’ve encountered them over and over and over again. I’m just picking on this particular speech because it’s been slathered all over the internet lately and I’ve encountered a lot of people who really resonated with its message.

  1. Science and engineering and math are treated as basically the same thing.  This. This is one of my biggest pet peeves when it comes to talking about science. Yes, I know that STEM fields (that’s Science, Technology, Engineering and Mathematics) are often lumped together. Yes, I know that there’s a lot of cross-pollination. But one, and only one, of these fields has as its goal the creation of testable models. And that’s science. The goal of engineering is to make stuff. And I know just enough math to know that there’s no way I know what the goal of mathematics is. The takeaway here is that, no matter how “science-y” they may seem, how enfolded they are into the science mystique, neither math nor engineering is a science. 
  2. There’s an insinuation that “science” =  thinking and “non-science” = NOT thinking.  This is really closely tied in with the idea that you have to be smart to be a scientist. False. Absolutely false. In fact, raw intelligence isn’t even on my list of the top five qualities you need to be a scientist:
    1. Passion. You need to love what you do, because otherwise being in grad school for five to ten years while living under the poverty line and working sixty hour weeks just isn’t worth it.
    2. Dedication. See above.
    3. Creativity. Good scientists ask good questions, and coming up with a good but answerable question that no one has asked before and  that will help shed new light on whatever it is you’re studying takes lateral thinking.
    4. Excellent time management skills. Particularly if you’re working in a university setting. You need to be able to balance research, teaching and service, all while still maintaining a healthy life. It’s hard.
    5.  Intelligibility. A huge part of science is taking very complex concepts and explaining them clearly. To your students. To other scientists. To people on the bus. To people on the internet (Hi guys!). You can have everything else on this list in spades, but if you can’t express your ideas you’re going to sink like a lead duck.
  3. Science is progress! Right? Right? Yes. Absolutely. There is no way in which science has harmed the human race and no way in which things other than science have aided it. It sounds really silly when you just come out and say it, doesn’t it? I mean, we have the knowledge to eradicate polio, but because of social and political factors it hadn’t happened yet. And you can’t solve social problems by just throwing science at them. And then there’s the fact that, while the models themselves maybe morally neutral, the uses to which they are put are not always so. See Einstein and the bomb. See chemical and biological warfare. And, frankly, I think the greatest advances of the 20th century weren’t in science or engineering or technology. They were deep-seated changes in how we, particularly Americans, treated people. My great-grandmother couldn’t go to high school because she was a woman. My mother couldn’t take college-level courses because she was a woman, though she’s currently working on her degree.  Now, I’m a graduate student and my gender is almost completely irrelevant. Segregation is over. Same sex relationships are legally acknowledged by nine states and DC. That’s the progress I would miss most if a weeping angel got me.
  4. Go quantitative or go home.  I’ve noticed a strong bias towards quantitative data, to the point that a lot of people argue that it’s better than qualitative data. I take umbridge at this. Quantitative data is easier, not necessarily better. Easier? Absolutely. It’s easier to get ten people to agree that a banana is ten inches than it does to agree that it’s tasty. And yet, from a practical standpoint, banana growers want to grow tastier bananas, ones that will ship well and sell well, not longer bananas. But it can be hard to plug “banana tastiness” into your mathematical models and measuring “tastiness” leaves you open to criticism that your data collection is biased. (That’s not to say that qualitative data can’t be biased.) This idea that quantitative data is better leads to an overemphasis on the type of questions that can best be answered quantitatively and that’s a problem. This also leads some people to dismiss the “squishy” sciences that use mainly qualitative data and that’s also a problem. All branches of science help us to shed new light on the world and universe around us and to ignore work because it doesn’t fit the science mystique is a grave mistake.

So what can we do to help lessen the effects of these biases? To disentangle the science mystique from the actual science? Well, the best thing we can do is be aware of it. Critically examine the ways the people talk about science. Closely examine your own biases. I, for example, find it far too easy to slip into the “quantitative is better” trap. Notice systematic similarities and question them. Science is, after all, about asking questions.

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